1100 Best Neural Networks Books of All Time
We've ranked the best neural networks books using expert recommendations, sales data, and millions of reader ratings. At Shortform, we know books. Our book guides are the best in the world. Learn why.
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2Deep Learning with Python
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.
In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.
In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.
3Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems
4Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
Recommended by Mark Tabladillo.Mark Tabladillo
Book to Start You on Machine Learning - KDnuggets https://t.co/19fdX59b0d This book is “Hands-On Machine Learning with Scikit-Learn & TensorFlow”. each new revision has become an even better version of one of the best in-depth resources to learn Machine Learning by doing. https://t.co/ujyUH3xU3e [source]
5Deep Learning
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Elon Musk
Founder/SpaceX
Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. [source]
Nassim Nicholas Taleb
Author
Very clear exposition, does the math without getting lost in the details. Although many of the concepts of the introductory first 100 pages can be found elsewhere, they are presented with remarkable cut-to-the-chase clarity. [source]
Satya Nadella
CEO/Microsoft
Elon Musk and Facebook AI chief Yann LeCun have praised this textbook on one of software’s most promising frontiers. After its publication, Microsoft signed up coauthor Bengio, a pioneer in machine learning, as an adviser [source]
6On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines
From the inventor of the PalmPilot comes a new and compelling theory of intelligence, brain function, and the future of intelligent machines
Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.
Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.
The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.
In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.
Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.
Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.
Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.
The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.
In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.
Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.
Recommended by Ev Williams and Joan Boixados.Joan Boixados
I’m reading “On intelligence” by Jeff Hawkins. I am really enjoying it. It’s a very specific theory of how our brain learns and makes predictions (the root of our intelligence) explained for average people unfamiliar with the field. It’s also very related to computer science and artificial intelligence since it tried to prove the current approaches to those are flawed. I’m getting a better understanding of how our brain works and how does our behaviour affects our thinking as much as our thinking affects our behaviour. Thus, it’s interesting to connect it with the idea of habits and how we can really benefit from them. [source]
7Neural Networks and Deep Learning
Neural Networks and Deep Learning is a free online book. The book will teach you about:
* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.
* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.
8The Hundred-Page Machine Learning Book
WARNING! To avoid buying counterfeit on Amazon, click on "See All Buying Options" and choose "Amazon.com" and not a third-party seller.
Concise and to the point — the book can be read during a week. During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.
Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.
Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!
Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.
Concise and to the point — the book can be read during a week. During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.
Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.
Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!
Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.
Recommended by Kirk Borne.Kirk Borne
Recent top-selling books in #AI & #MachineLearning: https://t.co/Ij9I7SzR4d ————— #BigData #DataScience #DataMining #Algorithms #PredictiveAnalytics #Python ————— ...in the TOP 10: 1)The Hundred-Page ML Book: https://t.co/dQ7nP6gwP0 2)Hands-on ML with...: https://t.co/Y0Iz3GbtGP https://t.co/72rAFN1FwW [source]
9The Book of Why: The New Science of Cause and Effect
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence
"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality--the study of cause and effect--on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
"Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality--the study of cause and effect--on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
Recommended by D.a. Wallach and Kirk Borne.D.a. Wallach
@EricTopol @yudapearl @bschoelkopf @MPI_IS I love @yudapearl 's book so much! Profound, heterodox. [source]
Kirk Borne
.@yudapearl wrote the awesome "Book of Why", but he recommends this fun and less #mathematics-heavy read >> his #AI lecture given in 1999: https://t.co/kNYIoJ8qcY #DataScience #MachineLearning #Statistics #BookofWhy #Causalinference #Bayes https://t.co/CNQlKP8cU3 [source]
10The Fabric of Reality: The Science of Parallel Universes--and Its Implications
For David Deutsch, a young physicist of unusual originality, quantum theory contains our most fundamental knowledge of the physical world. Taken literally, it implies that there are many universes “parallel” to the one we see around us. This multiplicity of universes, according to Deutsch, turns out to be the key to achieving a new worldview, one which synthesizes the theories of evolution, computation, and knowledge with quantum physics. Considered jointly, these four strands of explanation reveal a unified fabric of reality that is both objective and comprehensible, the subject of this daring, challenging book. The Fabric of Reality explains and connects many topics at the leading edge of current research and thinking, such as quantum computers (which work by effectively collaborating with their counterparts in other universes), the physics of time travel, the comprehensibility of nature and the physical limits of virtual reality, the significance of human life, and the ultimate fate of the universe. Here, for scientist and layperson alike, for philosopher, science-fiction reader, biologist, and computer expert, is a startlingly complete and rational synthesis of disciplines, and a new, optimistic message about existence.
Recommended by Chris Anderson, Chris Anderson, Chris Anderson, and 1 others.Chris Anderson
I honestly think it was reading this book that finally gave me the courage, 18 years ago, to leave my company and take over leadership of TED. [source]
Chris Anderson
I honestly think it was reading this book that finally gave me the courage, 18 years ago, to leave my company and take over leadership of TED. [source]
Chris Anderson
I honestly think it was reading this book that finally gave me the courage, 18 years ago, to leave my company and take over leadership of TED. [source]
11You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place
AS HEARD ON NPR'S "SCIENCE FRIDAY"
Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an "accessible, informative, and hilarious" introduction to the weird and wonderful world of artificial intelligence (Ryan North).
"You look like a thing and I love you" is one of the best pickup lines ever... according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.
We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really... and how does it solve problems, understand humans, and even drive self-driving cars?
Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't. Like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?
In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity.
You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.
"I can't think of a better way to learn about artificial intelligence, and I've never had so much fun along the way." - Adam Grant, New York Times bestselling author of Originals
Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an "accessible, informative, and hilarious" introduction to the weird and wonderful world of artificial intelligence (Ryan North).
"You look like a thing and I love you" is one of the best pickup lines ever... according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.
We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really... and how does it solve problems, understand humans, and even drive self-driving cars?
Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't. Like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?
In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity.
You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.
"I can't think of a better way to learn about artificial intelligence, and I've never had so much fun along the way." - Adam Grant, New York Times bestselling author of Originals
Recommended by Robert Went.Robert Went
Reading ‘You look like a thing and I love you: How AI works and why it is making the world a weirder place’, a wonderful book by @JanelleCShane — very funny, and I learn a lot https://t.co/SaZPjRTdVw [source]
12The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
Recommended by Vinod Khosla.Vinod Khosla
Co-Founder/Sun Microsystem
If you want speculation about what the master AI might need (one view). For a slightly more technical read, I’d suggest Ian Goodfellows Deep Learning. [source]
13The Nature of Code
How can we capture the unpredictable evolutionary and emergent properties of nature in software? How can understanding the mathematical principles behind our physical world help us to create digital worlds? This book focuses on a range of programming strategies and techniques behind computer simulations of natural systems, from elementary concepts in mathematics and physics to more advanced algorithms that enable sophisticated visual results. Readers will progress from building a basic physics engine to creating intelligent moving objects and complex systems, setting the foundation for further experiments in generative design. Subjects covered include forces, trigonometry, fractals, cellular automata, self-organization, and genetic algorithms. The book's examples are written in Processing, an open-source language and development environment built on top of the Java programming language. On the book's website (http://www.natureofcode.com), the examples run in the browser via Processing's JavaScript mode.
14Make Your First GAN With PyTorch
A gentle introduction to Generative Adversarial Networks, and a practical step-by-step tutorial on making your own with PyTorch.
GANs are one of the most exciting areas of machine learning, able to create entirely synthetic but surprising realistic images.
This beginner-friendly guide will give you hands-on experience:
learning PyTorch basics
developing your first PyTorch neural network
exploring neural network refinements to improve performance
introduce CUDA GPU acceleration
It will introduce GANs, one of the most exciting areas of machine learning:
introducing the concept step-by-step, in plain English
coding the simplest GAN to develop a good workflow
growing our confidence with an MNIST GAN
progressing to develop a GAN to generate full-colour human faces
experiencing how GANs fail, exploring remedies and improving GAN performance and stability
Beyond the very basics, readers can explore more sophisticated GANs:
convolutional GANs for generated higher quality images
conditional GANs for generated images of a desired class
The appendices will be useful for students of machine learning as they explain themes often skipped over in many courses:
calculating ideal loss values for balanced GANs
probability distributions and sampling them to create images
carefully chosen examples illustrating how convolutions work
a brief explanation of why gradient descent isn't suited to adversarial machine learning
All code is available publicly as open source on github.
GANs are one of the most exciting areas of machine learning, able to create entirely synthetic but surprising realistic images.
This beginner-friendly guide will give you hands-on experience:
learning PyTorch basics
developing your first PyTorch neural network
exploring neural network refinements to improve performance
introduce CUDA GPU acceleration
It will introduce GANs, one of the most exciting areas of machine learning:
introducing the concept step-by-step, in plain English
coding the simplest GAN to develop a good workflow
growing our confidence with an MNIST GAN
progressing to develop a GAN to generate full-colour human faces
experiencing how GANs fail, exploring remedies and improving GAN performance and stability
Beyond the very basics, readers can explore more sophisticated GANs:
convolutional GANs for generated higher quality images
conditional GANs for generated images of a desired class
The appendices will be useful for students of machine learning as they explain themes often skipped over in many courses:
calculating ideal loss values for balanced GANs
probability distributions and sampling them to create images
carefully chosen examples illustrating how convolutions work
a brief explanation of why gradient descent isn't suited to adversarial machine learning
All code is available publicly as open source on github.
15Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence
"The authors' clear visual style provides a comprehensive look at what's currently possible with artificial neural networks as well as a glimpse of the magic that's to come."
--Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning
Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline's techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn.
World-class instructor and practitioner Jon Krohn--with visionary content from Grant Beyleveld and beautiful illustrations by Agla� Bassens--presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered.
You'll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms.
Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
--Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning
Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline's techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn.
World-class instructor and practitioner Jon Krohn--with visionary content from Grant Beyleveld and beautiful illustrations by Agla� Bassens--presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered.
You'll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms.
Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Recommended by Kirk Borne.Kirk Borne
🌟📘📊📈Awesome new book >> #DeepLearning Illustrated — A Visual, Interactive Guide to Artificial Intelligence” https://t.co/xIW48MskrR by @JonKrohnLearns ——————— #BigData #Analytics #DataScience #AI #MachineLearning #Algorithms #NeuralNetworks https://t.co/JKSrVRLpS0 [source]
16Neural Networks for Pattern Recognition
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
17Neural Networks for Kids (Tinker Toddlers)
18Learning From Data: A Short Course
Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.
19Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Machine Learning Recommendations.
Learn how to build recommender systems from one of Amazon's pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies. You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them. This book is adapted from Frank's popular online course published by Sundog Education, so you can expect lots of visual aids from its slides and a conversational, accessible tone throughout the book. The graphics and scripts from over 300 slides are included, and you'll have access to all of the source code associated with it as well. We'll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data. This book is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. We'll cover: -Building a recommendation engine -Evaluating recommender systems -Content-based filtering using item attributes -Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF -Model-based methods including matrix factorization and SVD -Applying deep learning, AI, and artificial neural networks to recommendations -Session-based recommendations with recursive neural networks -Scaling to massive data sets with Apache
20Building Machine Learning Powered Applications: Going from Idea to Product
Learn the skills necessary to design, build, and deploy applications powered by machine learning. Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step.
Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science's AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book's example application.
The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.
This book will help you:
Determine your product goal and set up a machine learning problem
Build your first end-to-end pipeline quickly and acquire an initial dataset
Train and evaluate your ML model and address performance bottlenecks
Deploy and monitor models in a production environment
Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science's AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book's example application.
The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.
This book will help you:
Determine your product goal and set up a machine learning problem
Build your first end-to-end pipeline quickly and acquire an initial dataset
Train and evaluate your ML model and address performance bottlenecks
Deploy and monitor models in a production environment
21Artificial Intelligence: A Modern Approach
For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa
22Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects
23Pattern Classification
The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Recommended by Eric Weinstein.Eric Weinstein
Managing Director/Thiel Capital
Eric Weinstein recommended this book on Twitter. [source]
24Machine Learning with Neural Networks Using MATLAB
25A Guide to Convolutional Neural Networks for Computer Vision
Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision.
This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation.
This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.
This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation.
This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.
26Grokking Deep Learning
Artificial Intelligence is one of the most exciting technologies of the century, and Deep Learning is in many ways the “brain” behind some of the world’s smartest Artificial Intelligence systems out there. Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.
Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.
Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.
27The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind
Our minds are working all the time, but we rarely stop to think about how they work. The human mind has many different ways to think, says Marvin Minsky, the leading figure in artificial intelligence and computer science. We use these different ways of thinking in different circumstances, and some of them we don't even associate with thinking. For example, emotions, intuitions, and feelings are just other forms of thinking, according to Minsky. In his groundbreaking new work, "The Emotion Machine," Minsky shows why we should expand our ideas about thinking and how thinking itself might change in the future."The Emotion Machine" explains how our minds work, how they progress from simple kinds of thought to more complex forms that enable us to reflect on ourselves -- what most people refer to as consciousness, or self-awareness. Unlike other broad theories of the mind, this book proceeds in a step-by-step fashion that draws on detailed and specific examples. It shows that thinking -- even higher-level thinking -- can be broken down into a series of specific actions. From emotional states to goals and attachments and on to consciousness and awareness of self, we can understand the process of thinking in all its intricacy. And once we understand thinking, we can build machines -- artificial intelligences -- that can assist with our thinking, machines that can follow the same thinking patterns that we follow and that can think as we do. These humanlike thinking machines would also be emotion machines -- just as we are.
This is a brilliant book that challenges many ideas about thinking and the mind. It is as insightful and provocative as it is original, the fruit of a lifetime spentthinking about thinking.
This is a brilliant book that challenges many ideas about thinking and the mind. It is as insightful and provocative as it is original, the fruit of a lifetime spentthinking about thinking.
Recommended by Sebastian Seung.Sebastian Seung
Marvin Minsky is famous as one of the fathers of artificial intelligence. The Emotion Machine is a summation of his lifetime of thinking about how the brain works. It is written somewhat as a computer scientist but also as a philosopher and psychologist. He’s a brilliant thinker and a brilliant writer. Most people will not know that he actually started out as a neuroscientist. But he gave up on neuroscience, deciding that if he wanted to understand intelligence in his lifetime he had better not study the brain but try to construct it in computer models. [source]
28Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers
Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size--small enough to work on the digital signal processor in an Android phone. With this practical book, you'll learn about TensorFlow Lite for Microcontrollers, a miniscule machine learning library that allows you to run machine learning algorithms on tiny hardware.
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
Understand how to work with Arduino and ultralow-power microcontrollers
Use techniques for optimizing latency, energy usage, and model and binary size
Authors Pete Warden and Daniel Situnayake explain how you can train models that are small enough to fit into any environment, including small embedded devices that can run for a year or more on a single coin cell battery. Ideal for software and hardware developers who want to build embedded devices using machine learning, this guide shows you how to create a TinyML project step-by-step. No machine learning or microcontroller experience is necessary.
Learn practical machine learning applications on embedded devices, including simple uses such as speech recognition and gesture detection
Train models such as speech, accelerometer, and image recognition, you can deploy on Arduino and other embedded platforms
Understand how to work with Arduino and ultralow-power microcontrollers
Use techniques for optimizing latency, energy usage, and model and binary size
29Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
Link to the GitHub Repository containing the code examples and additional material: https://github.com/rasbt/python-machi...
Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.
You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world
Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.
You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world
30Build a Career in Data Science
Build a Career in Data Science is your guide to getting your first data science job, then quickly becoming a senior employee. Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills you’ll need alongside your technical know-how in order to succeed in the field. Following their clear and simple instructions you’ll craft a resume that hiring managers will love, learn how to ace your interview, and ensure you hit the ground running in your first months at your new job. Once you’ve gotten your foot in the door, learn to thrive as a data scientist by handling high expectations, dealing with stakeholders, and managing failures. Finally, you’ll look towards the future and learn about how to join the broader data science community, leaving a job gracefully, and plotting your career path. With this book by your side you’ll have everything you need to ensure a rewarding and productive role in data science.
31Information Theory, Inference and Learning Algorithms
Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.
32Neural Network Methods for Natural Language Processing
Table of Contents:
Preface
Acknowledgments
Introduction
Learning Basics and Linear Models
From Linear Models to Multi-layer Perceptrons
Feed-forward Neural Networks
Neural Network Training
Features for Textual Data
Case Studies of NLP Features
From Textual Features to Inputs
Language Modeling
Pre-trained Word Representations
Using Word Embeddings
Case Study: A Feed-forward Architecture for Sentence Meaning Inference
Ngram Detectors: Convolutional Neural Networks
Recurrent Neural Networks: Modeling Sequences and Stacks
Concrete Recurrent Neural Network Architectures
Modeling with Recurrent Networks
Conditioned Generation
Modeling Trees with Recursive Neural Networks
Structured Output Prediction
Cascaded, Multi-task and Semi-supervised Learning
Conclusion
Bibliography
Author's Biography
Preface
Acknowledgments
Introduction
Learning Basics and Linear Models
From Linear Models to Multi-layer Perceptrons
Feed-forward Neural Networks
Neural Network Training
Features for Textual Data
Case Studies of NLP Features
From Textual Features to Inputs
Language Modeling
Pre-trained Word Representations
Using Word Embeddings
Case Study: A Feed-forward Architecture for Sentence Meaning Inference
Ngram Detectors: Convolutional Neural Networks
Recurrent Neural Networks: Modeling Sequences and Stacks
Concrete Recurrent Neural Network Architectures
Modeling with Recurrent Networks
Conditioned Generation
Modeling Trees with Recursive Neural Networks
Structured Output Prediction
Cascaded, Multi-task and Semi-supervised Learning
Conclusion
Bibliography
Author's Biography
33Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
34Programming Collective Intelligence: Building Smart Web 2.0 Applications
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.
Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:
Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you.
"Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."
-- Dan Russell, Google
"Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."
-- Tim Wolters, CTO, Collective Intellect
Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:
Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you.
"Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."
-- Dan Russell, Google
"Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."
-- Tim Wolters, CTO, Collective Intellect
35Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & Tensorflow
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.
Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.
Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite
Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral
Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies
Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning
Use transfer learning to train models in minutes
Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.
Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite
Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral
Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies
Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning
Use transfer learning to train models in minutes
Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
36Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Volume 1: Foundations
What makes people smarter than computers? The work described in these two volumes suggests that the answer lies in the massively parallel architecture of the human mind. It is some of the most exciting work in cognitive science, unifying neural and cognitive processes in a highly computational framework, with links to artificial intelligence. Although thought and problem solving have a sequential character when viewed over a time frame of minutes or hours, the authors argue that each step in the sequence is the result of the simultaneous activity of a large number of simple computational elements, each influencing others and being influenced by them. "Parallel Distributed Processing" describes their work in developing a theoretical framework for describing this parallel distributed processing activity and in applying the framework to the development of models of aspects of perception, memory, language, and thought.
Volume 1 lays the theoretical foundations of parallel distributed processing. It introduces the approach and the reasons why the authors feel it is a fruitful one, describes several models of basic mechanisms with wide applicability to different problems, and presents a number of specific technical analyses of different aspects of parallel distributed models.
Volume 1 lays the theoretical foundations of parallel distributed processing. It introduces the approach and the reasons why the authors feel it is a fruitful one, describes several models of basic mechanisms with wide applicability to different problems, and presents a number of specific technical analyses of different aspects of parallel distributed models.
37The Math of Neural Networks
There are many reasons why neural networks fascinate us and have captivated headlines in recent years. They make web searches better, organize photos, and are even used in speech translation. Heck, they can even generate encryption. At the same time, they are also mysterious and mind-bending: how exactly do they accomplish these things ? What goes on inside a neural network? On a high level, a network learns just like we do, through trial and error. This is true regardless if the network is supervised, unsupervised, or semi-supervised. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. In the following chapters we will unpack the mathematics that drive a neural network. To do this, we will use a feedforward network as our model and follow input as it moves through the network.
38Deep Learning with R
Summary
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks.
About the Book
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.
What's Inside
Deep learning from first principles
Setting up your own deep-learning environment
Image classification and generation
Deep learning for text and sequences
About the Reader
You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.
About the Authors
François Chollet is a deep-learning researcher at Google and the author of the Keras library.
J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras.
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks.
About the Book
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.
What's Inside
Deep learning from first principles
Setting up your own deep-learning environment
Image classification and generation
Deep learning for text and sequences
About the Reader
You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.
About the Authors
François Chollet is a deep-learning researcher at Google and the author of the Keras library.
J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras.
39Artificial Life: A Report from the Frontier Where Computers Meet Biology
This enthralling book alerts us to nothing less than the existence of new varieties of life. Some of these species can move and eat, see, reproduce, and die. Some behave like birds or ants. One such life form may turn out to be our best weapon in the war against AIDS.
What these species have in common is that they exist inside computers, their DNA is digital, and they have come into being not through God's agency but through the efforts of a generation of scientists who seek to create life in silico.
But even as it introduces us to these brilliant heretics and unravels the intricacies of their work. Artificial Life examines its subject's dizzying philosophical implications: Is a self-replicating computer program any less alive than a flu virus? Are carbon-and-water-based entities merely part of the continuum of living things? And is it possible that one day "a-life" will look back at human beings and dismiss us as an evolutionary way station -- or, worse still, a dead end?
What these species have in common is that they exist inside computers, their DNA is digital, and they have come into being not through God's agency but through the efforts of a generation of scientists who seek to create life in silico.
But even as it introduces us to these brilliant heretics and unravels the intricacies of their work. Artificial Life examines its subject's dizzying philosophical implications: Is a self-replicating computer program any less alive than a flu virus? Are carbon-and-water-based entities merely part of the continuum of living things? And is it possible that one day "a-life" will look back at human beings and dismiss us as an evolutionary way station -- or, worse still, a dead end?
40Architects of Intelligence: The truth about AI from the people building it
41What Happens Next? Conversations from MARS
The most innovative minds in science and technology reveal a vision for the future of life on Earth - and beyond.
Every year, 200 experts across machine learning, automation, robotics, and space arrive in Palm Springs for MARS - the yearly, invitation-only event hosted by Amazon founder and CEO Jeff Bezos - to share new ideas about how these four fields will shape our future.
In What Happens Next: Conversations from MARS, TV host and maker of things Adam Savage (MythBusters) takes listeners deep inside the MARS conference. In interviews with more than a dozen leading scientists and thinkers - including former astronaut Mike Massimino, iRobot co-founder Rodney Brooks, Dava Newman (former deputy administrator of NASA), Oren Etzioni (CEO, Allen Institute for Artificial Intelligence), futurist Kate Compton, and Caleb Harper (director of the Open Agriculture initiative at MIT) - Savage explores the mind-blowing and often misunderstood ways in which science and innovation are transforming the way we live, work, and play.
Full of wonder, optimism, and plain old awesomeness, What Happens Next will be a revelation for anyone who’s ever wondered about what our future will look like and how we’ll get there.
2 hours 50 minutes
Every year, 200 experts across machine learning, automation, robotics, and space arrive in Palm Springs for MARS - the yearly, invitation-only event hosted by Amazon founder and CEO Jeff Bezos - to share new ideas about how these four fields will shape our future.
In What Happens Next: Conversations from MARS, TV host and maker of things Adam Savage (MythBusters) takes listeners deep inside the MARS conference. In interviews with more than a dozen leading scientists and thinkers - including former astronaut Mike Massimino, iRobot co-founder Rodney Brooks, Dava Newman (former deputy administrator of NASA), Oren Etzioni (CEO, Allen Institute for Artificial Intelligence), futurist Kate Compton, and Caleb Harper (director of the Open Agriculture initiative at MIT) - Savage explores the mind-blowing and often misunderstood ways in which science and innovation are transforming the way we live, work, and play.
Full of wonder, optimism, and plain old awesomeness, What Happens Next will be a revelation for anyone who’s ever wondered about what our future will look like and how we’ll get there.
2 hours 50 minutes
42Fundamentals of Neural Networks: Architectures, Algorithms and Applications
Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.
43Augmented Human: How Technology Is Shaping the New Reality
Augmented Reality (AR) blurs the boundary between the physical and digital worlds. In AR's current exploration phase, innovators are beginning to create compelling and contextually rich applications that enhance a user's everyday experiences. In this book, Dr. Helen Papagiannis--a world-leading expert in the field--introduces you to AR: how it's evolving, where the opportunities are, and where it's headed.
If you're a designer, developer, entrepreneur, student, educator, business leader, artist, or simply curious about AR's possibilities, this insightful guide explains how you can become involved with an exciting, fast-moving technology.
You'll explore how:
Computer vision, machine learning, cameras, sensors, and wearables change the way you see the world
Haptic technology syncs what you see with how something feels
Augmented sound and hearables alter the way you listen to your environment
Digital smell and taste augment the way you share and receive information
New approaches to storytelling immerse and engage users more deeply
Users can augment their bodies with electronic textiles, embedded technology, and brain-controlled interfaces
Human avatars can learn our behaviors and act on our behalf
If you're a designer, developer, entrepreneur, student, educator, business leader, artist, or simply curious about AR's possibilities, this insightful guide explains how you can become involved with an exciting, fast-moving technology.
You'll explore how:
Computer vision, machine learning, cameras, sensors, and wearables change the way you see the world
Haptic technology syncs what you see with how something feels
Augmented sound and hearables alter the way you listen to your environment
Digital smell and taste augment the way you share and receive information
New approaches to storytelling immerse and engage users more deeply
Users can augment their bodies with electronic textiles, embedded technology, and brain-controlled interfaces
Human avatars can learn our behaviors and act on our behalf
44Deep Learning: A Practitioner's Approach
Looking for one central source where you can learn key findings on machine learning? Deep Learning: The Definitive Guide provides developers and data scientists with the most practical information available on the subject, including deep learning theory, best practices, and use cases.
Authors Adam Gibson and Josh Patterson present the latest relevant papers and techniques in a nonacademic manner, and implement the core mathematics in their DL4J library. If you work in the embedded, desktop, and big data/Hadoop spaces and really want to understand deep learning, this is your book.
Authors Adam Gibson and Josh Patterson present the latest relevant papers and techniques in a nonacademic manner, and implement the core mathematics in their DL4J library. If you work in the embedded, desktop, and big data/Hadoop spaces and really want to understand deep learning, this is your book.
45Compassionate Artificial Intelligence
The book describes the principles, algorithms and frameworks for developing meaningful compassionate AI systems. Compassionate AI address the issues for creating solutions for some of the challenges the humanity is facing today, like the need for compassionate care-giving, helping physically and mentally challenged people, reducing human pain and diseases, stopping nuclear warfare, preventing mass destruction weapons, tackling terrorism and stopping the exploitation of innocent citizens by monster governments through digital surveillance. The book also talks about compassionate AI for precision medicine, new drug discovery, education, and legal system. Dr. Ray explained the DeepCompassion algorithms, five design principles and eleven key behavioral principle of compassionate AI systems. The book also explained several compassionate AI projects.
Compassionate AI is the best practical guide for AI students, researchers, entrepreneurs, business leaders looking to get true value from the adoption of compassion in machine learning technology.
Compassionate AI is the best practical guide for AI students, researchers, entrepreneurs, business leaders looking to get true value from the adoption of compassion in machine learning technology.
46Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition
47Neural Networks, Fuzzy Logic And Genetic Algorithms: Synthesis And Applications
48Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition).
This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.
This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.
Recommended by Kirk Borne.Kirk Borne
5-★ #DataScientists should enjoy this classic #MachineLearning book! >> “Neural Smithing — Supervised Learning in Feedforward Artificial #NeuralNetworks” ————— Get it here: https://t.co/SWRpWbxSkr ————— #DataScience #BigData #DeepLearning #AI #Algorithms https://t.co/Q8dlEFat1c [source]
49Make Your Own Neural Network: An In-depth Visual Introduction For Beginners
A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.
50Natural Language Processing in Action
Natural Language Processing in Action is your guide to creating machines that understand human language using the power of Python with its ecosystem of packages dedicated to NLP and AI! You'll start with a mental model of how a computer learns to read and interpret language. Then, you'll discover how to train a Python-based NLP machine to recognize patterns and extract information from text. As you explore the carefully-chosen examples, you'll expand your machine's knowledge and apply it to a range of challenges, from building a search engine that can find documents based on their meaning rather than merely keywords, to training a chatbot that uses deep learning to answer questions and participate in a conversation.
51Machine Learning: An Applied Mathematics Introduction
52Fundamentals of Artificial Neural Networks
Hassoun provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers.
As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Fundamentals of Artificial Neural Networks, he provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers. Such a systematic and unified treatment, although sadly lacking in most recent texts on neural networks, makes the subject more accessible to students and practitioners. Here, important results are integrated in order to more fully explain a wide range of existing empirical observations and commonly used heuristics. There are numerous illustrative examples, over 200 end-of-chapter analytical and computer-based problems that will aid in the development of neural network analysis and design skills, and a bibliography of nearly 700 references. Proceeding in a clear and logical fashion, the first two chapters present the basic building blocks and concepts of artificial neural networks and analyze the computational capabilities of the basic network architectures involved. Supervised, reinforcement, and unsupervised learning rules in simple nets are brought together in a common framework in chapter three. The convergence and solution properties of these learning rules are then treated mathematically in chapter four, using the average learning equation analysis approach. This organization of material makes it natural to switch into learning multilayer nets using backprop and its variants, described in chapter five. Chapter six covers most of the major neural network paradigms, while associative memories and energy minimizing nets are given detailed coverage in the next chapter. The final chapter takes up Boltzmann machines and Boltzmann learning along with other global search/optimization algorithms such as stochastic gradient search, simulated annealing, and genetic algorithms.
As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years. Now, in Fundamentals of Artificial Neural Networks, he provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers. Such a systematic and unified treatment, although sadly lacking in most recent texts on neural networks, makes the subject more accessible to students and practitioners. Here, important results are integrated in order to more fully explain a wide range of existing empirical observations and commonly used heuristics. There are numerous illustrative examples, over 200 end-of-chapter analytical and computer-based problems that will aid in the development of neural network analysis and design skills, and a bibliography of nearly 700 references. Proceeding in a clear and logical fashion, the first two chapters present the basic building blocks and concepts of artificial neural networks and analyze the computational capabilities of the basic network architectures involved. Supervised, reinforcement, and unsupervised learning rules in simple nets are brought together in a common framework in chapter three. The convergence and solution properties of these learning rules are then treated mathematically in chapter four, using the average learning equation analysis approach. This organization of material makes it natural to switch into learning multilayer nets using backprop and its variants, described in chapter five. Chapter six covers most of the major neural network paradigms, while associative memories and energy minimizing nets are given detailed coverage in the next chapter. The final chapter takes up Boltzmann machines and Boltzmann learning along with other global search/optimization algorithms such as stochastic gradient search, simulated annealing, and genetic algorithms.
53Neural Network Design
The well-known, respected authors who developed the Neural Networks toolbox and the Fuzzy Systems Toolbox now bring you this text designed for electrical and computer engineering or computer science beginners. The book covers neuron model and network architectures, signal and weight vector spaces, linear transformations for neural networks. and performance surfaces and optimum points.
54Machine Learning Pocket Reference: A Quick Guide to Structured Machine Learning Techniques
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.
Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.
This pocket reference includes sections that cover:
Classification, using the Titanic dataset
Cleaning data and dealing with missing data
Exploratory data analysis
Common preprocessing steps using sample data
Selecting features useful to the model
Model selection
Metrics and classification evaluation
Regression examples using k-nearest neighbor, decision trees, boosting, and more
Metrics for regression evaluation
Clustering
Dimensionality reduction
Scikit-learn pipelines
Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics.
This pocket reference includes sections that cover:
Classification, using the Titanic dataset
Cleaning data and dealing with missing data
Exploratory data analysis
Common preprocessing steps using sample data
Selecting features useful to the model
Model selection
Metrics and classification evaluation
Regression examples using k-nearest neighbor, decision trees, boosting, and more
Metrics for regression evaluation
Clustering
Dimensionality reduction
Scikit-learn pipelines
55Programming: 4 Manuscripts in 1 book: Python For Beginners - Python 3 Guide - Learn Java - Excel 2016
56Deep Learning from Scratch: Building with Python from First Principles
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You'll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.
Author Seth Weidman shows you how neural networks work using a first principles approach. You'll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you'll be set up for success on all future deep learning projects.
This book provides:
Extremely clear and thorough mental models--accompanied by working code examples and mathematical explanations--for understanding neural networks
Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework
Working implementations and clear-cut explanations of convolutional and recurrent neural networks
Implementation of these neural network concepts using the popular PyTorch framework
Author Seth Weidman shows you how neural networks work using a first principles approach. You'll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you'll be set up for success on all future deep learning projects.
This book provides:
Extremely clear and thorough mental models--accompanied by working code examples and mathematical explanations--for understanding neural networks
Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework
Working implementations and clear-cut explanations of convolutional and recurrent neural networks
Implementation of these neural network concepts using the popular PyTorch framework
57Hands-On Deep Learning with Go: A practical guide to building and implementing neural network models using Go
58Deep Learning
59Deep Learning with JavaScript: Neural networks in TensorFlow.js
Summary
Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.
Foreword by Nikhil Thorat and Daniel Smilkov.
About the technology
Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack.
About the book
In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation.
What's inside
- Image and language processing in the browser
- Tuning ML models with client-side data
- Text and image creation with generative deep learning
- Source code samples to test and modify
About the reader
For JavaScript programmers interested in deep learning.
About the author
Shanging Cai, Stanley Bileschi and Eric D. Nielsen are software engineers with experience on the Google Brain team, and were crucial to the development of the high-level API of TensorFlow.js. This book is based in part on the classic, Deep Learning with Python by François Chollet.
TOC:
PART 1 - MOTIVATION AND BASIC CONCEPTS
1 • Deep learning and JavaScript
PART 2 - A GENTLE INTRODUCTION TO TENSORFLOW.JS
2 • Getting started: Simple linear regression in TensorFlow.js
3 • Adding nonlinearity: Beyond weighted sums
4 • Recognizing images and sounds using convnets
5 • Transfer learning: Reusing pretrained neural networks
PART 3 - ADVANCED DEEP LEARNING WITH TENSORFLOW.JS
6 • Working with data
7 • Visualizing data and models
8 • Underfitting, overfitting, and the universal workflow of machine learning
9 • Deep learning for sequences and text
10 • Generative deep learning
11 • Basics of deep reinforcement learning
PART 4 - SUMMARY AND CLOSING WORDS
12 • Testing, optimizing, and deploying models
13 • Summary, conclusions, and beyond
Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.
Foreword by Nikhil Thorat and Daniel Smilkov.
About the technology
Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack.
About the book
In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation.
What's inside
- Image and language processing in the browser
- Tuning ML models with client-side data
- Text and image creation with generative deep learning
- Source code samples to test and modify
About the reader
For JavaScript programmers interested in deep learning.
About the author
Shanging Cai, Stanley Bileschi and Eric D. Nielsen are software engineers with experience on the Google Brain team, and were crucial to the development of the high-level API of TensorFlow.js. This book is based in part on the classic, Deep Learning with Python by François Chollet.
TOC:
PART 1 - MOTIVATION AND BASIC CONCEPTS
1 • Deep learning and JavaScript
PART 2 - A GENTLE INTRODUCTION TO TENSORFLOW.JS
2 • Getting started: Simple linear regression in TensorFlow.js
3 • Adding nonlinearity: Beyond weighted sums
4 • Recognizing images and sounds using convnets
5 • Transfer learning: Reusing pretrained neural networks
PART 3 - ADVANCED DEEP LEARNING WITH TENSORFLOW.JS
6 • Working with data
7 • Visualizing data and models
8 • Underfitting, overfitting, and the universal workflow of machine learning
9 • Deep learning for sequences and text
10 • Generative deep learning
11 • Basics of deep reinforcement learning
PART 4 - SUMMARY AND CLOSING WORDS
12 • Testing, optimizing, and deploying models
13 • Summary, conclusions, and beyond
60Predictive Analytics: The Secret to Predicting Future Events Using Big Data and Data Science Techniques Such as Data Mining, Predictive Modelling, Statistics, Data Analysis, and Machine Learning
61The Essence Of Neural Networks
The aim of this work is to cover the basic concepts, with the key neural network models explored sufficiently deeply to allow a competent programmer to implement the networks in a language of their choice. The book is supported by a website.
62Neural Networks
Neural Networks provides a gentle introduction to the subject, for undergraduates from Computer Science and Electrical Engineering degrees.
This updated and revised second edition assumes no prior knowledge and sets out to describe what neural nets are, what they do, and how they do it. The main networks covered include ADALINE, WISARD, the Hopfield Network, Bidirectional Associative Memory, the Boltzmann machine, counter-propogation and ART networks, and Kohonen's self-organizing maps. These networks are discussed by means of examples, giving the reader a good overall knowledge of current developments in the field.
This updated and revised second edition assumes no prior knowledge and sets out to describe what neural nets are, what they do, and how they do it. The main networks covered include ADALINE, WISARD, the Hopfield Network, Bidirectional Associative Memory, the Boltzmann machine, counter-propogation and ART networks, and Kohonen's self-organizing maps. These networks are discussed by means of examples, giving the reader a good overall knowledge of current developments in the field.
63A Brief Introduction to Neural Networks
Neural networks are a bio-inspired mechanism of data processing, that enables computers to learn technically similar to a brain and even generalize once solutions to enough problem instances are taught.
64Studies in Computational Intelligence, Volume 32: Complex-Valued Neural Networks
This monograph instructs graduate- and undergraduate-level students in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering on the concepts of complex-valued neural networks. Emphasizing basic concepts and ways of thinking about neural networks, the author focuses on neural networks that deal with complex numbers; the practical advantages of complex-valued neural networks, and their origins; the development of principal applications? The book uses detailed examples to answer these questions and more.
65Neural Networks for Beginners: An Easy Textbook for Machine Learning Fundamentals to Guide You Implementing Neural Networks with Python and Deep Learning (Artificial Intelligence)
66Computational Intelligence: Concepts to Implementations
Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence.
This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications.
This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems.
This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications.
This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems.
67Neural Networks Theory
This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. It details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis. The theory is expansive: covering not just traditional topics such as network architecture but also neural continua in function spaces as well.
68Pattern Recognition and Neural Networks
Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.
69Deep Learning and the Game of Go
70Neural Networks And Learning Machines
71An Introduction to Neural Networks
This key user-friendly feature notwithstanding, the book provides a full level of explanation of the technical aspects of the subject, which non-mathematical rivals usually fail to provide, thereby leaving those areas obscure. Although the study of neural networks is underpinned by ideas that are often best described mathematically, the fundamentals of the subject are accessible without the full mathematical apparatus, as this treatment amply demonstrates.
72A Tiny Bite of Murder (The Monkey's Eyebrow Tea Room #1)
73R Programming: A Step-by-Step Guide for Absolute Beginners
74Programming Pytorch for Deep Learning: Creating and Deploying Deep Learning Applications
Take the next steps toward mastering deep learning, the machine learning method that's transforming the world around us by the second. In this practical book, you'll get up to speed on key ideas using Facebook's open source PyTorch framework and gain the latest skills you need to create your very own neural networks.
Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.
Learn how to deploy deep learning models to production
Explore PyTorch use cases from several leading companies
Learn how to apply transfer learning to images
Apply cutting-edge NLP techniques using a model trained on Wikipedia
Use PyTorch's torchaudio library to classify audio data with a convolutional-based model
Debug PyTorch models using TensorBoard and flame graphs
Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud
Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production.
Learn how to deploy deep learning models to production
Explore PyTorch use cases from several leading companies
Learn how to apply transfer learning to images
Apply cutting-edge NLP techniques using a model trained on Wikipedia
Use PyTorch's torchaudio library to classify audio data with a convolutional-based model
Debug PyTorch models using TensorBoard and flame graphs
Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud
75Data Science for Business 2019 (2 BOOKS IN 1): Master Data Analytics & Machine Learning with Optimized Marketing Strategies (Artificial Intelligence, Neural ... Networks, Algorithms & Predictive Modelling
76Networks: A Very Short Introduction
From ecosystems to Facebook, from the Internet to the global financial market, some of the most important and familiar natural systems and social phenomena are based on a networked structure. It is impossible to understand the spread of an epidemic, a computer virus, large-scale blackouts, or massive extinctions without taking into account the network structure that underlies all these phenomena. In this Very Short Introduction, Guido Caldarelli and Michele Catanzaro discuss the nature and variety of networks, using everyday examples from society, technology, nature, and history to explain and understand the science of network theory. They show the ubiquitous role of networks; how networks self-organize; why the rich get richer; and how networks can spontaneously collapse. They conclude by highlighting how the findings of complex network theory have very wide and important applications in genetics, ecology, communications, economics, and sociology.
77Programming Neural Networks with Encog 3 in C#
Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the C# programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering to gain new insights into data. Neural network architectures such as feedforward, self organizing maps, NEAT, and recurrent neural networks are introduced. This book also covers advanced neural network training techniques such as back propagation, quick propagation, resilient propagation, Levenberg Marquardt, genetic training and simulated annealing. Real world problems such as financial prediction, classifiction and image processing are introduced.
78Artificial Intelligence Engines
The brain has always had a fundamental advantage over conventional computers: it can learn. However, a new generation of artificial intelligence algorithms, in the form of deep neural networks, is rapidly eliminating that advantage. Deep neural networks rely on adaptive algorithms to master a wide variety of tasks, including cancer diagnosis, object recognition, speech recognition, robotic control, chess, poker, backgammon and Go, at super-human levels of performance.
In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (perceptrons, Hopfield nets, Boltzmann machines and backpropagation networks), and modern deep neural networks (variational autoencoders, convolutional networks, generative adversarial networks, and reinforcement learning using SARSA and Q-learning). Online computer programs, collated from open source repositories, give hands-on experience of neural networks, and PowerPoint slides provide support for teaching. Written in an informal style, with a comprehensive glossary, tutorial appendices (e.g. Bayes' theorem, maximum likelihood estimation), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.
In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (perceptrons, Hopfield nets, Boltzmann machines and backpropagation networks), and modern deep neural networks (variational autoencoders, convolutional networks, generative adversarial networks, and reinforcement learning using SARSA and Q-learning). Online computer programs, collated from open source repositories, give hands-on experience of neural networks, and PowerPoint slides provide support for teaching. Written in an informal style, with a comprehensive glossary, tutorial appendices (e.g. Bayes' theorem, maximum likelihood estimation), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.
79A Brief Guide to Artificial Intelligence
Artificial intelligence (AI) has now mastered tasks that until recently could be performed only by humans. These tasks include cancer diagnosis, drug design, object recognition, speech recognition, and playing chess, backgammon and Go, which AI systems perform at superhuman levels.
This richly illustrated book is a brief but comprehensive overview (without equations) of current AI systems, how they work, their applications, and their limitations. After surveying the impressive capabilities of AI systems in certain domains, the limited ability of AI to perform tasks that humans find trivial is discussed. Finally, the question of whether AI systems can be intelligent is considered, along with the controversial issue of machine consciousness. Written in an informal style, with a comprehensive Glossary and a list of Further Readings, this book is an ideal introduction to the rapidly evolving field of AI.
This richly illustrated book is a brief but comprehensive overview (without equations) of current AI systems, how they work, their applications, and their limitations. After surveying the impressive capabilities of AI systems in certain domains, the limited ability of AI to perform tasks that humans find trivial is discussed. Finally, the question of whether AI systems can be intelligent is considered, along with the controversial issue of machine consciousness. Written in an informal style, with a comprehensive Glossary and a list of Further Readings, this book is an ideal introduction to the rapidly evolving field of AI.
80Machine Learning with TensorFlow
Summary
Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.
About the Book
Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.
What's Inside
Matching your tasks to the right machine-learning and deep-learning approaches
Visualizing algorithms with TensorBoard
Understanding and using neural networks
About the Reader
Written for developers experienced with Python and algebraic concepts like vectors and matrices.
About the Author
Author Nishant Shukla is a computer vision researcher focused on applying machine-learning techniques in robotics.
Senior technical editor, Kenneth Fricklas, is a seasoned developer, author, and machine-learning practitioner.
Table of Contents
PART 1 - YOUR MACHINE-LEARNING RIG
A machine-learning odyssey
TensorFlow essentials
PART 2 - CORE LEARNING ALGORITHMS
Linear regression and beyond
A gentle introduction to classification
Automatically clustering data
Hidden Markov models
PART 3 - THE NEURAL NETWORK PARADIGM
A peek into autoencoders
Reinforcement learning
Convolutional neural networks
Recurrent neural networks
Sequence-to-sequence models for chatbots
Utility landscape
Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.
About the Book
Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.
What's Inside
Matching your tasks to the right machine-learning and deep-learning approaches
Visualizing algorithms with TensorBoard
Understanding and using neural networks
About the Reader
Written for developers experienced with Python and algebraic concepts like vectors and matrices.
About the Author
Author Nishant Shukla is a computer vision researcher focused on applying machine-learning techniques in robotics.
Senior technical editor, Kenneth Fricklas, is a seasoned developer, author, and machine-learning practitioner.
Table of Contents
PART 1 - YOUR MACHINE-LEARNING RIG
A machine-learning odyssey
TensorFlow essentials
PART 2 - CORE LEARNING ALGORITHMS
Linear regression and beyond
A gentle introduction to classification
Automatically clustering data
Hidden Markov models
PART 3 - THE NEURAL NETWORK PARADIGM
A peek into autoencoders
Reinforcement learning
Convolutional neural networks
Recurrent neural networks
Sequence-to-sequence models for chatbots
Utility landscape
81MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence
Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book.
With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.
What You'll Learn
Use MATLAB for deep learning
Discover neural networks and multi-layer neural networks
Work with convolution and pooling layers
Build a MNIST example with these layers
Who This Book Is For
Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.
With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage.
What You'll Learn
Use MATLAB for deep learning
Discover neural networks and multi-layer neural networks
Work with convolution and pooling layers
Build a MNIST example with these layers
Who This Book Is For
Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.
82Practical Neural Network Recipes in C++
This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assumed and all models are presented from the ground up.
The principle focus of the book is the three layer feedforward network, for more than a decade as the workhorse of professional arsenals. Other network models with strong performance records are also included.
Bound in the book is an IBM diskette that includes the source code for all programs in the book. Much of this code can be easily adapted to C compilers. In addition, the operation of all programs is thoroughly discussed both in the text and in the comments within the code to facilitate translation to other languages.
The principle focus of the book is the three layer feedforward network, for more than a decade as the workhorse of professional arsenals. Other network models with strong performance records are also included.
Bound in the book is an IBM diskette that includes the source code for all programs in the book. Much of this code can be easily adapted to C compilers. In addition, the operation of all programs is thoroughly discussed both in the text and in the comments within the code to facilitate translation to other languages.
83Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python
84Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
85Introduction to the Math of Neural Networks
86Common LISP Modules: Artificial Intelligence in the Era of Neural Networks and Chaos Theory
While creativity plays an important role in the advancement of computer science, great ideas are built on a foundation of practical experience and knowledge. This book presents programming techniques which will be useful in both AI projects and more conventional software engineering endeavors. My primary goal is to enter- tain, to introduce new technologies and to provide reusable software modules for the computer programmer who enjoys using programs as models for solutions to hard and interesting problems. If this book succeeds in entertaining, then it will certainly also educate. I selected the example application areas covered here for their difficulty and have provided both program examples for specific applications and (I hope) the method- ology and spirit required to master problems for which there is no obvious solution. I developed the example programs on a Macintosh (TM) using the Macintosh Common LISP (TM) development system capturing screen images while the example programs were executing. To ensure portability to all Common LISP environments, I have provided a portable graphics library in Chapter 2. All programs in this book are copyrighted by Mark Watson. They can be freely used in any free or commercial software systems if the following notice appears in the fine print of the program's documentation: "This program contains software written by Mark Watson." No royalties are required. The program miniatures contained in this book may not be distributed by posting in source code form on public information networks, or in printed form without my written permission.
87Neural Networks: A Systematic Introduction
Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.
88Big Data: A Guide to Big Data Trends, Artificial Intelligence, Machine Learning, Predictive Analytics, Internet of Things, Data Science, Data Analytics, Business Intelligence, and Data Mining
89Deep Learning, Vol. 1: From Basics to Practice
People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions.
The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Table of Contents below.
Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn't in the programming. Rather, it's knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today's tools, those choices require understanding what's going on under the hood.
This book is designed to give you that understanding. You'll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You'll be able to read and understand the documentation for whatever library you'd like to use. And you'll be able to follow exciting, on-going breakthroughs as they appear, because you'll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning.
The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use.
You don't need any previous experience with machine learning or deep learning for this book. You don't need to be a mathematician, because there's nothing in the book harder than the occasional multiplication. You don't need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware.
Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure.
Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code.
The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice.
Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more.
This friendly, informal book puts those tools into your pocket.
For a more information, see https://www.dlbasics.com
Table of Contents
-- Volume 1 --
1 Introduction to Machine Learning
2 Statistics
3 Probability
4 Bayes' Rule
5 Curves And Surfaces
6 Information Theory
7 Classification
8 Training And Testing
9 Overfitting And Underfitting
10 Neurons
11 Learning And Reasoning
12 Data Preparation
13 Classifiers
14 Ensembles
15 Scikit-Learn
16 Feed Forward Networks
17
The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Table of Contents below.
Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn't in the programming. Rather, it's knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today's tools, those choices require understanding what's going on under the hood.
This book is designed to give you that understanding. You'll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You'll be able to read and understand the documentation for whatever library you'd like to use. And you'll be able to follow exciting, on-going breakthroughs as they appear, because you'll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning.
The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use.
You don't need any previous experience with machine learning or deep learning for this book. You don't need to be a mathematician, because there's nothing in the book harder than the occasional multiplication. You don't need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware.
Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure.
Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code.
The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice.
Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more.
This friendly, informal book puts those tools into your pocket.
For a more information, see https://www.dlbasics.com
Table of Contents
-- Volume 1 --
1 Introduction to Machine Learning
2 Statistics
3 Probability
4 Bayes' Rule
5 Curves And Surfaces
6 Information Theory
7 Classification
8 Training And Testing
9 Overfitting And Underfitting
10 Neurons
11 Learning And Reasoning
12 Data Preparation
13 Classifiers
14 Ensembles
15 Scikit-Learn
16 Feed Forward Networks
17
90Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover.
Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.
Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning
Set up and manage machine learning projects end-to-end
Build an anomaly detection system to catch credit card fraud
Clusters users into distinct and homogeneous groups
Perform semisupervised learning
Develop movie recommender systems using restricted Boltzmann machines
Generate synthetic images using generative adversarial networks
Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started.
Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning
Set up and manage machine learning projects end-to-end
Build an anomaly detection system to catch credit card fraud
Clusters users into distinct and homogeneous groups
Perform semisupervised learning
Develop movie recommender systems using restricted Boltzmann machines
Generate synthetic images using generative adversarial networks
91Calculus Essentials for Dummies
Calculus Essentials For Dummies (9781119591207) was previously published as Calculus Essentials For Dummies (9780470618356). While this version features a new Dummies cover and design, the content is the same as the prior release and should not be considered a new or updated product.
Many colleges and universities require students to take at least one math course, and Calculus I is often the chosen option. Calculus Essentials For Dummies provides explanations of key concepts for students who may have taken calculus in high school and want to review the most important concepts as they gear up for a faster-paced college course. Free of review and ramp-up material, Calculus Essentials For Dummies sticks to the point with content focused on key topics only. It provides discrete explanations of critical concepts taught in a typical two-semester high school calculus class or a college level Calculus I course, from limits and differentiation to integration and infinite series. This guide is also a perfect reference for parents who need to review critical calculus concepts as they help high school students with homework assignments, as well as for adult learners headed back into the classroom who just need a refresher of the core concepts.
The Essentials For Dummies Series
Dummies is proud to present our new series, The Essentials For Dummies. Now students who are prepping for exams, preparing to study new material, or who just need a refresher can have a concise, easy-to-understand review guide that covers an entire course by concentrating solely on the most important concepts. From algebra and chemistry to grammar and Spanish, our expert authors focus on the skills students most need to succeed in a subject.
Many colleges and universities require students to take at least one math course, and Calculus I is often the chosen option. Calculus Essentials For Dummies provides explanations of key concepts for students who may have taken calculus in high school and want to review the most important concepts as they gear up for a faster-paced college course. Free of review and ramp-up material, Calculus Essentials For Dummies sticks to the point with content focused on key topics only. It provides discrete explanations of critical concepts taught in a typical two-semester high school calculus class or a college level Calculus I course, from limits and differentiation to integration and infinite series. This guide is also a perfect reference for parents who need to review critical calculus concepts as they help high school students with homework assignments, as well as for adult learners headed back into the classroom who just need a refresher of the core concepts.
The Essentials For Dummies Series
Dummies is proud to present our new series, The Essentials For Dummies. Now students who are prepping for exams, preparing to study new material, or who just need a refresher can have a concise, easy-to-understand review guide that covers an entire course by concentrating solely on the most important concepts. From algebra and chemistry to grammar and Spanish, our expert authors focus on the skills students most need to succeed in a subject.
92AI for Game Developers
Advances in 3D visualization and physics-based simulation technology make it possible for game developers to create compelling, visually immersive gaming environments that were only dreamed of years ago. But today's game players have grown in sophistication along with the games they play. It's no longer enough to wow your players with dazzling graphics; the next step in creating even more immersive games is improved artificial intelligence, or AI.
Fortunately, advanced AI game techniques are within the grasp of every game developer--not just those who dedicate their careers to AI. If you're new to game programming or if you're an experienced game programmer who needs to get up to speed quickly on AI techniques, you'll find AI for Game Developers to be the perfect starting point for understanding and applying AI techniques to your games.
Written for the novice AI programmer, AI for Game Developers introduces you to techniques such as finite state machines, fuzzy logic, neural networks, and many others, in straightforward, easy-to-understand language, supported with code samples throughout the entire book (written in C/C++). From basic techniques such as chasing and evading, pattern movement, and flocking to genetic algorithms, the book presents a mix of deterministic (traditional) and non-deterministic (newer) AI techniques aimed squarely at beginners AI developers. Other topics covered in the book include:
Potential function based movements: a technique that handles chasing, evading swarming, and collision avoidance simultaneously Basic pathfinding and waypoints, including an entire chapter devoted to the A* pathfinding algorithm AI scripting Rule-based AI: learn about variants other than fuzzy logic and finite state machines Basic probability Bayesian techniques
Unlike other books on the subject, AI for Game Developers doesn't attempt to cover every aspect of game AI, but to provide you with usable, advanced techniques you can apply to your games right now. If you've wanted to use AI to extend the play-life of your games, make them more challenging, and most importantly, make them more fun, then this book is for you.
Fortunately, advanced AI game techniques are within the grasp of every game developer--not just those who dedicate their careers to AI. If you're new to game programming or if you're an experienced game programmer who needs to get up to speed quickly on AI techniques, you'll find AI for Game Developers to be the perfect starting point for understanding and applying AI techniques to your games.
Written for the novice AI programmer, AI for Game Developers introduces you to techniques such as finite state machines, fuzzy logic, neural networks, and many others, in straightforward, easy-to-understand language, supported with code samples throughout the entire book (written in C/C++). From basic techniques such as chasing and evading, pattern movement, and flocking to genetic algorithms, the book presents a mix of deterministic (traditional) and non-deterministic (newer) AI techniques aimed squarely at beginners AI developers. Other topics covered in the book include:
Potential function based movements: a technique that handles chasing, evading swarming, and collision avoidance simultaneously Basic pathfinding and waypoints, including an entire chapter devoted to the A* pathfinding algorithm AI scripting Rule-based AI: learn about variants other than fuzzy logic and finite state machines Basic probability Bayesian techniques
Unlike other books on the subject, AI for Game Developers doesn't attempt to cover every aspect of game AI, but to provide you with usable, advanced techniques you can apply to your games right now. If you've wanted to use AI to extend the play-life of your games, make them more challenging, and most importantly, make them more fun, then this book is for you.
93Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
94Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks
95Hands-On Deep Learning with R: A practical guide to designing, building, and improving neural network models using R
96Nmap 7: From Beginner to Pro
97Algorithms: The Complete Guide To The Computer Science & Artificial Intelligence Used to Solve Human Decisions, Advance Technology, Optimize Habits, Learn Faster & Your Improve Life (Two Book Bundle)
98Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks
Deep belief nets are one of the most exciting recent developments in artificial intelligence. The structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ‘thought process’ that is capable of learning abstract concepts built from simpler primitives. A typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. This book presents the essential building blocks of the most common forms of deep belief nets. At each step the text provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the DEEP program which implements these algorithms, are available for free download from the author’s website.
99Learn Keras for Deep Neural Networks: A Fast-Track Approach to Modern Deep Learning with Python
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.
The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets.
Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning.
At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
What You'll Learn
Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks Who This Book Is For
Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.
The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets.
Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning.
At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
What You'll Learn
Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks Who This Book Is For
Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.
100