99 Best Neural Networks Books of All Time
We've researched and ranked the best neural networks books in the world, based on recommendations from world experts, sales data, and millions of reader ratings. Learn more
Featuring recommendations from Elon Musk, Satya Nadella, Nassim Nicholas Taleb, and 22 other experts. 
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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... 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... more
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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...
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Recommended by Mark Tabladillo, and 1 others. Mark TabladilloBook 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)
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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... more
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... more
Nassim Nicholas TalebVery 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 NadellaElon 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)
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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... more
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... more
Joan BoixadosI’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... (Source)
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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... more
* 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... more
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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.
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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.
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Recommended by Kirk Borne, and 1 others. Kirk BorneRecent 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)
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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,... more
"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,... more
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)
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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... more
Chris AndersonI 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 AndersonI 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 AndersonI 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)
Don't have time to read the top Neural Networks books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
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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,... more
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,... more
Recommended by Robert Went, and 1 others. Robert WentReading ‘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)
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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... more
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... more
Recommended by Vinod Khosla, and 1 others. Vinod KhoslaIf 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)
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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... more
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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... more
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... more
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"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... more
--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... more
Recommended by Kirk Borne, and 1 others. 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)
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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... more
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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... more
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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... more
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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,... more
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,... more
Don't have time to read the top Neural Networks books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
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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,... more
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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.
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Recommended by Eric Weinstein, and 1 others. Eric Weinstein[Eric Weinstein recommended this book on Twitter.] (Source)
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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... more
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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... more
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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... more
Recommended by Sebastian Seung, and 1 others. Sebastian SeungMarvin 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... (Source)
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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... more
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... more
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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... more
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... more
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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... more
Don't have time to read the top Neural Networks books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
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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... more
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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... more
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... more
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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... more
Programming Collective Intelligence takes you into the world of machine learning... more
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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... more
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... more
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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... more
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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... more
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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... more
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... more
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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... more
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... more
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Don't have time to read the top Neural Networks books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
41
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... more
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... more
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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. less
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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... more
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... more
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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. less
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. less
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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... more
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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). more
Recommended by Kirk Borne, and 1 others. Kirk Borne5-★ #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)
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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... more
50
Don't have time to read the top Neural Networks books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
51
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... more
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... more
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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. less
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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... more
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... more
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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,... more
Author Seth Weidman shows you how neural networks work using a first principles approach. You'll learn how to apply multilayer neural networks,... more
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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.
... more
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.
... more
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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. less
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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... more
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... more
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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. less
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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. less
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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... more
This book lays emphasis... more
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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. less
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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... more
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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. less
Don't have time to read the top Neural Networks books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
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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... more
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... more
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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... more
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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,... more
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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... more
In this richly illustrated book, key neural network learning algorithms are explained informally first, followed... more
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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... more
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... more
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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... more
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... more
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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... more
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... more
Don't have time to read the top Neural Networks books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
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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... more
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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... more
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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... more
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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:... more
Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks:... more
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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... more
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... more
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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... more
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... more
Don't have time to read the top Neural Networks books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
91
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... more
Fortunately, advanced AI game techniques are within the grasp of every game developer--not just those who dedicate their careers to AI. If... more
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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... more
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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... more
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... more
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Don't have time to read the top Neural Networks books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.