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.
1Cover unavailable
2Deep 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]
Cover unavailable
3Hands-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]
Cover unavailable
4Deep 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.
5Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems
Cover unavailable
6Neural 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.
Cover unavailable
7The 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]
8The 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]
9Reinforcement Learning: An Introduction
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Recommended by Zachary Lipton.Zachary Lipton
@innerproduct 1. Tor Lattimore Great book work on bandits (https://t.co/gttspSm40W) and work on causality + bandits (https://t.co/lkwvtEiKvE) 2. Caroline Uhler — Interesting work on causal inference + discovery, causal inference under measurement error etc (https://t.co/I3IRpwmdMd) [source]
10The 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]
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]
12On 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]
13Scary Smart: Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World
Cover unavailable
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.
Cover unavailable
15Artificial 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
Cover unavailable
16The 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.
17The Agentic AI Bible: The Complete and Up-to-Date Guide to Design, Develop, and Scale Goal-Driven, LLM-Powered Agents that Think, Execute and Evolve
The Complete Engineering Blueprint for Designing and Scaling LLM-Powered Autonomous AgentsAgent-based AI is no longer a futuristic dream—it’s the next phase of intelligent software.Yet for most teams, the path to building real-world agentic systems is filled with chaos: bloated prototypes that can’t scale, brittle tools duct-taped together, unclear architectures, and zero visibility into performance or safety.Most technical leaders are left asking the same questions:How do we move beyond demos to dependable systems?How do we orchestrate perception, reasoning, and action in complex environments?How do we design agents that can think, execute, and evolve—while remaining safe, observable, and maintainable?The Agentic AI Bible delivers clear, practical answers.Designed for engineers, system architects, and AI product leads, this comprehensive guide breaks down the entire lifecycle of agentic systems—from foundational design principles to deployment and real-world application—backed by current best practices, production-ready tools, and future-facing insights.Here is a mere fraction of what you will learn:The core principles and modular architecture patterns that power modern agentic systems so you can design agents with reasoning, memory, and planning from the ground upHow to implement LLM-powered agents with perception, action, and environment loops to create systems that can interact with the world, not just respond to promptsAdvanced behavioral patterns such as recursive reasoning, self-reflection, and goal reprioritization to ensure agents can adapt in real time3 secret design strategies for safety, reliability, and testability that prevent catastrophic errors and keep your agents under controlHow to connect your agents with external tools, APIs, and long-term workflows so they can handle multi-step tasks with real business impact6 benchmarking frameworks and evaluation metrics that help you measure intelligence, robustness, and operational readiness in complex settingsReal-world applications across industries—from finance to robotics—and how to tailor agent capabilities to domain-specific needsDeployment architectures, scaling strategies, and monitoring systems that ensure your agents don’t just work, but continue improving over timeAnd much, much more!While other books skim the surface or focus narrowly on theory, The Agentic AI Bible delivers the full lifecycle—from architecture to deployment to governance—designed for professionals who need working systems, not academic demos.Whether you're building a personal AI assistant, a robotic process orchestrator, or a full-blown autonomous multi-agent system, this book offers the technical blueprint you need to deliver robust, goal-driven, LLM-powered agents that perform at scale.If you're ready to move past academic toy projects and start delivering agentic AI that works in production, this is the guide you've been waiting for.
18Understanding Deep Learning
Cover unavailable
19Neural 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.
20Build a Large Language Model (From Scratch)
21The 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]
Cover unavailable
22Building 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
Cover unavailable
23Neural Networks for Kids (Tinker Toddlers)
Cover unavailable
24Building 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
Cover unavailable
25Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects
26The StatQuest Illustrated Guide To Machine Learning
27Pattern 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]
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
Cover unavailable
29A 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.
Cover unavailable
30Machine Learning with Neural Networks Using MATLAB
31Deep 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]
32LLM Engineer's Handbook: Master the art of engineering large language models from concept to production
33Artificial Intelligence Bible (3-in-1): AI Agents, Prompt Engineering & Generative AI. Automate & Scale — Beginner's Guide to Slash Costs, Save Time & Accelerate Growth: ChatGPT Insights Included
Unlock the power of Artificial Intelligence — no jargon, no overwhelm.If you’re ready to save time, slash costs, and grow faster in a world moving at AI speed, this 3-in-1 guide is your ultimate shortcut. Whether you’re a solo entrepreneur, a leader navigating digital change, or just AI-curious, this audiobook distills the core systems behind AI Agents, Prompt Engineering, and Generative AI into a powerful, beginner-friendly format.What’s inside?You’ll learn how to:Use AI Agents to handle routine tasks and free up hours every week.Prompt ChatGPT, Claude, and Gemini with precision — no guesswork.Generate content, visuals, audio, and even data — faster than ever.Automate your workstreams without hiring or coding.Stay ahead of risk, bias, and AI hallucinations with simple safeguards.This isn’t a theory-heavy lecture. It’s a high-impact toolkit made for action. Expect quick wins, vivid metaphors, and practical walkthroughs you can apply the same day — even if you’ve never touched an AI tool before.And here’s what makes this edition stand out:The insights evolve. The content is structured to reflect the most current breakthroughs — with regular updates over time to keep you ahead as new tools, models, and rules emerge. So you're not stuck in yesterday’s AI.If you’ve been flooded with hype but starved of clarity, this is your fast on-ramp. Designed for listening, built for doing.Don’t wait to catch up — lead the change.Buy Artificial Intelligence Bible [3-in-1] now and start building your AI advantage today.PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
34The All in One Agentic AI Bible: The Practical 7 Step Guide To Building Smarter AI Agents, Designing Goal-Oriented, LLM-Powered Systems That Think, Act, And Adapt In The Real World
Updated 2026 Edition — A Complete Engineering Blueprint For Building Production-Ready AI Agents Stop Building Chatbots. Start Engineering Autonomous Systems.Most “AI guides” teach prompts.This book teaches architecture.If you want to design AI agents that plan, reason, execute tools, manage memory, coordinate roles, and operate safely in real-world environments — this is your field manual.The All In One Agentic AI Bible is a structured 7-step framework that takes you from foundational concepts to enterprise-grade deployment. Whether you’re an engineer, founder, product leader, or AI strategist, this guide shows you how to build systems that move beyond conversation into intelligent action.What You’ll Master Inside:A Clear Foundation Of Agentic AI — The Four Core Building Blocks: Perception, Reasoning, Action, And FeedbackFrom Chatbots To Autonomous Systems — How Modern Agents Evolved From Deterministic Rules To Probabilistic PlanningMemory Architectures That Work — Token Budgets, Long-Term Persistence, Context Compression, And Drift PreventionBuilding Your First Real Agent — Selecting The Stack, Integrating Tools, Function Calling, Debugging Cognitive FailuresMultimodal And External Actions — Browser Control, APIs, RPA, Real-World Execution BlueprintsSecurity And Robustness — Injection Attacks, Jailbreak Defense, Tool Risk, And Hardening StrategiesDomain-Specific Agents — Business Operations, Healthcare Intelligence, Finance, Robotics, Creative WorkEnterprise Deployment — Hosting Models, Observability, Cost Tuning, Compliance, Governance, And Risk ManagementHuman-Agent Collaboration — Oversight Models, Transparency, Trust Design, And Scalable AdoptionThis Is Not Theory.Every module builds toward production-ready systems. You’ll understand coordination mechanisms, sequential vs parallel workflows, role-based agent systems (Coordinator, Worker, Delegator), evaluation frameworks, benchmarking architecture, and long-term scalability strategies.BONUS: The Agentic AI Implementation SuiteAccelerate your development with five production-ready toolkits included with this book:The Agent ROI & Use-Case Matrix: A Time-Motion Calculator and 5-slide pitch deck to prove value and greenlight your projects.The Key Schema Vault: 10 plug-and-play JSON schemas for core architecture, including Delegators and Memory Managers.The Master System Prompt Library: 12 battle-tested prompts for specialized roles, anti-hallucination, and universal guardrails.The Red Teaming Security Protocol: A complete adversarial suite with 7 injection attacks and a 12-point security audit.The Production Readiness Audit: Your final safety gate featuring a Latency & Cost Calculator and a 10-prompt Stress Test.Why This Book Is DifferentIt Bridges Research And Implementation — No Abstract Concepts Without Engineering TranslationIt Focuses On Systems Thinking — Not Prompts, But ArchitectureIt Covers The Entire Lifecycle — From Design To Deployment To GovernanceIt Treats Security, Compliance, And Risk As Core Features — Not AfterthoughtsIt Prepares You For The Agent Economy — Not Just Today’s ToolsThe Future Of AI Is Not Better Chat.It’s Goal-Oriented Agents That Think, Act, Adapt, And Collaborate.If You Want To Build Systems That Operate With Intelligence — Not Just Generate Text — This Is Your Blueprint. Scroll Up, Click “Buy Now,” And Start Building AI Agents That Actually Work In The Real World.
35LLM Systems Engineering: Training and Building Large Language Models – Engineering AI Models Through Fine-Tuning, Continued Pretraining, and From-Scratch Development (AI Engineering)
"LLMs are highly general purpose technology for statistical modeling of token streams... If you can reduce your problem to that of modeling token streams, you can 'throw an LLM at it.'"– Andrej Karpathy, founding member of OpenAILarge language models have become the defining technology of the current decade, powering tools that write and reason at scale. However, there is much more to LLMs than just the chat interface of ChatGPT or Claude: a deeper opportunity to design and adapt models to your own specific needs.Research scientist and author Rob McKinsey presents a detailed resource on the topic, spanning 42 modules organized into eight major chapters. The discussion begins with the inner workings of an LLM and progresses to data engineering and training production models with Python and modern engineering tools.Main Topics• How tokens, embeddings, and the transformer architecture turn text into model behavior• How pretraining objectives and inference loops actually work• How to plan data and compute budgets for an LLM project• How to fine-tune, continue pretraining, and train from scratch with Python and UnslothFeatures• Detailed code examples• 60+ visualizations and figures• 170+ primary research citationsWho This Book Is ForThis book is written for developers, AI architects, and enthusiasts who want a deeper, foundational understanding of how large language models are composed and trained. It assumes comfort with general programming and basic ideas from linear algebra and probability. The material is presented at an intermediate to advanced level.Table of Contents• Chapter I – Introduction to Large Language Models• Chapter II – Strategic and Technical Planning• Chapter III – Data Engineering• Chapter IV – Training Systems and Model Development• Chapter V – Fine-Tuning• Chapter VI – Continued Pretraining• Chapter VII – Training from Scratch• Chapter VIII – Evaluation, Monitoring, and MaintenanceWhether you are learning the fundamentals of LLM systems or training and adapting models in professional environments, this book provides a structured and practical guide to understanding and designing language models that perform reliably across real-world workflows.Scroll up and click Buy Now to start reading.
36Machine Learning for Trading: A disciplined workflow from research to live execution, with nine case studies and AI agents
Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAPKey FeaturesBuild point-in-time pipelines, integrate alternative data, and ensure data integrityBuild and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signalsDeploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generatorsBook DescriptionThe rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning. It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems. You’ll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna. Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets. By the end of this book, you’ll be proficient to build your own industrial-grade “alpha factory".What you will learnTransform raw data into predictive alpha factors, validated with leak-proof cross-validationMaster advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agentsHarness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standardsBuild production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safelyWho this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required. Table of ContentsThe Process is Your EdgeThe Financial Data UniverseMarket MicrostructureFundamental and Alternative DataSynthetic Financial DataStrategy Research FrameworkDefining the Learning TaskFinancial Feature EngineeringModel-Based Feature ExtractionText Feature EngineeringThe ML PipelineAdvanced Models for Tabular DataDeep Learning for Time SeriesLatent Factor ModelsCausal Machine LearningStrategy SimulationPortfolio ConstructionTransaction CostsRisk ManagementStrategy SynthesisReinforcement LearningRAG for Financial ResearchKnowledge GraphsAutonomous AgentsLive Trading SystemsMLOps and GovernanceThe Systematic Edge
37Learning 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.
38Build 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.
39The AI Engineering Bible: The Complete and Up-to-Date Guide to Build, Deploy and Scale Production Ready AI Systems
A Comprehensive, Practical Guide to Building, Deploying, and Scaling Real-World AI SystemsWhile AI dominates headlines, most organizations face a different reality: stalled projects, fragile infrastructure, costly deployments, and no clear framework for building scalable, reliable systems.The AI Engineering Bible addresses this gap directly.Written for engineers, technical leads, AI architects, and product owners, this book offers a clear, systematic approach to building production-ready AI systems—grounded in current best practices, scalable infrastructure, and real-world application.Spanning every stage of the AI lifecycle—from problem definition and data acquisition to deployment, optimization, and long-term maintenance—it provides the structure and technical depth professionals need to confidently lead AI initiatives at scale.With this all-in-one guide in your hands, you will:Start by defining the problem and planning your AI system with precision—from aligning goals with business outcomes to structuring architecture, data strategy, ethics, compliance, and human-AI interaction from day oneBuild each layer of your system with reliability in mind, including data pipelines, preprocessing workflows, training loops, orchestration tools, and model selection—ready for integration into real-world software environmentsDeploy your AI models into production with confidence, using containerized services, scalable cloud infrastructure, secure API integrations, and version-controlled workflows that reduce downtime and riskExpand your system to handle increasing scale, applying proven strategies for distributed inference, federated learning, pipeline throughput, and load balancing—ensuring your architecture grows without bottlenecksOptimize performance across every dimension, from latency and throughput to memory usage and cost-efficiency, using cutting-edge techniques in tuning, compression, quantization, and system profilingEnsure long-term reliability and adaptability through model monitoring, drift detection, retraining strategies, user feedback loops, governance frameworks, and continuous improvement processes that keep systems stable and effective over timeAnd much, much more!While other books focus narrowly on theory or specific tools, The AI Engineering Bible takes a full-stack engineering perspective—helping you bridge the gap between machine learning research and robust, maintainable production systems.Whether you're responsible for building internal AI platforms, deploying customer-facing features, or scaling intelligent systems in high-stakes environments, this book is designed to support your work with clarity and rigor.If your goal is to deliver AI systems that are not only functional, but sustainable, secure, and scalable—grab your copy of The AI Engineering Bible and use it as your trusted technical reference to build systems that perform in the real world.
40Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems
The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.With an approachable yet deeply informative style, author Aurélien Géron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorch—from basic regression techniques to advanced neural networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to build intelligent systems.Understand ML basics, including concepts like overfitting and hyperparameter tuningComplete an end-to-end ML project using scikit-Learn, covering everything from data exploration to model evaluationLearn techniques for unsupervised learning, such as clustering and anomaly detectionBuild advanced architectures like transformers and diffusion models with PyTorchHarness the power of pretrained models—including LLMs—and learn to fine-tune themTrain autonomous agents using reinforcement learning
41Deep Learning with Python, Second Edition
42The Hundred-Page Language Models Book: hands-on with PyTorch (The Hundred-Page Books)
43Practical 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
Cover unavailable
44The 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]
45The 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.
46AI for Beginners: Understanding Artificial Intelligence for Non-Tech Professionals: Discover How AI Works and How You Can Use It in Everyday Life and Work - No Coding Required
AI for Beginners: Understanding Artificial Intelligence for Non-Tech ProfessionalsDiscover How AI Works, How to Use It at Work and in Everyday Life, and What to Watch Out For. No Coding Required Artificial Intelligence is everywhere, but for many people, it still feels confusing, intimidating, or out of reach. This book is written for professionals, entrepreneurs, students, and curious listeners who want to understand artificial intelligence without becoming technical or learning to code. You don’t need a computer science degree. You don’t need to write a single line of code. You need clear explanations and real-world examples.In AI for Beginners, you’ll learn how to:Understand what AI really is (and what it isn’t) in plain EnglishRecognize how artificial intelligence already shows up in everyday life and workUnderstand the difference between AI, machine learning, and generative AIUse popular AI tools confidently without codingWrite better prompts to get more useful results from AI toolsAvoid common risks like misinformation, privacy issues, and over-reliance on AIUnderstand how AI is changing jobs and careers, and how to adaptStart learning and using AI without feeling overwhelmedWhat makes this book different:Written specifically for non-technical professionalsNo jargon, no math, no programmingPractical examples instead of theoryReal-world use cases you can relate toHands-on exercises and simple workflowsFocus on confidence, clarity, and responsible AI useWhether you want to work more efficiently, understand the technology shaping your career, or stop feeling left behind, this book gives you the foundation you need.Scroll up and start building AI confidence one chapter at a time.
47Grokking 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.
48Information 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.
49How AI Works: From Sorcery to Science
AI isn’t magic. How AI Works demystifies the explosion of artificial intelligence by explaining—without a single mathematical equation—what happened, when it happened, why it happened, how it happened, and what AI is actually doing "under the hood."Artificial intelligence is everywhere—from self-driving cars, to image generation from text, to the unexpected power of language systems like ChatGPT—yet few people seem to know how it all really works. How AI Works unravels the mysteries of artificial intelligence, without the complex math and unnecessary jargon.You’ll learn: The relationship between artificial intelligence, machine learning, and deep learningThe history behind AI and why the artificial intelligence revolution is happening nowHow decades of work in symbolic AI failed and opened the door for the emergence of neural networksWhat neural networks are, how they are trained, and why all the wonder of modern AI boils down to a simple, repeated unit that knows how to multiply input numbers to produce an output number.The implications of large language models, like ChatGPT and Bard, on our society—nothing will be the same againAI isn’t magic. If you’ve ever wondered how it works, what it can do, or why there’s so much hype, How AI Works will teach you everything you want to know.
50The StatQuest Illustrated Guide to Neural Networks and AI: With hands-on examples in PyTorch!!!
Cover unavailable
51Neural 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
52Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
53Python 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
Cover unavailable
54Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
55Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
56Understanding Quantum Computing: A Guide to Quantum Computing in Plain English for the Curious Professional (Understanding Quantum Computing for Everyone)
You have read the headlines. Quantum computing will crack today’s encryption. It will simulate molecules no supercomputer can touch. It will solve optimization problems that have stumped logistics networks for decades. Every time you try to follow the explanation, the jargon arrives like a wall — qubits, superposition, decoherence, entanglement — and the tab gets closed. Meanwhile, the boardrooms, research labs, and defense agencies that understand this technology are already making decisions that will reshape your industry, your data, and your career. The gap between those who understand quantum computing and those who do not is widening by the quarter. This book closes that gap.Inside this book, readers will learn how to:• Understand qubits and superposition — why a quantum bit is fundamentally different from a classical bit, and why that single difference changes the scale of what computing can do• Grasp quantum entanglement and follow how two particles separated by distance can share information in ways that defy classical intuition — and power entirely new communication systems• Navigate quantum cryptography and post-quantum encryption — understand why today’s RSA and AES security standards have an expiration date and what organizations must do before it arrives• Apply quantum algorithms and optimization concepts to real business problems in logistics, finance, pharmaceutical discovery, and artificial intelligence without requiring a physics background• Evaluate qubit hardware and decoherence — distinguish between superconducting qubits, photonic systems, and trapped-ion platforms and understand why stability remains the central engineering challenge• Recognize quantum advantage — learn precisely when and why a quantum computer outperforms a classical system, and when classical computing remains the smarter choice• Understand classical computing limitations and identify the exact categories of problems — where quantum approaches are already superior• Build emerging technology strategy as a business leader or decision-maker by evaluating quantum readiness, vendor claims, and investment timing with a clear analytical framework• Explore quantum sensing and simulation and discover how quantum systems are already being deployed in medical imaging, materials science, and climate modeling todayThis book follows Priya Anand, CEO of Meridian Dynamics, and her team — a CTO, a data scientist, and a quantum strategist — as they move from skepticism to strategy across the chapters. Their questions are your questions. Their breakthroughs are your roadmap. By the time their story concludes, you will not just understand what quantum computing is — you will understand what it means for your organization, your profession, and the world reshaping itself around you.No physics degree required. No advanced mathematics needed. Every concept is introduced through analogy, narrative, and real-world application before any technical term is defined. The science is real. The explanations are human. This is the book that makes quantum computing genuinely accessible to business leaders, engineers, healthcare professionals, policy readers, students, and anyone who refuses to accept that this revolution is not for them.The encryption protects your financial data. The drug discovery pipeline targeting diseases that defeat today’s medicine. The logistics networks that move global supply chains. The national security systems that define the balance of technological power. All of it is being transformed. The question is no longer whether quantum computing will matter to you.The question is whether you will understand it in time to act.Open this book. The revolution has already begun — qubit by qubit, algorithm by algorithm, breakthrough by breakthrough. Your seat at the table starts here.
Cover unavailable
57Parallel 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.
Cover unavailable
58Programming 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
59Generative AI System Design Interview
60Architects of Intelligence: The truth about AI from the people building it
61The Ultimate Generative AI for Beginners Collection: The 3-in-1 Guide to Learn AI, Deep Learning & ChatGPT in Just Minutes a
Cover unavailable
62Deep 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.
Cover unavailable
63Artificial 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?
Cover unavailable
64What 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
65AI for Beginners: Effortlessly Learn AI Essentials and Master Cutting-Edge Trends in Just 10 Days to Future-Proof Your Skills
Cover unavailable
66Fundamentals 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.
67Agentic Artificial Intelligence: Harnessing AI Agents to Reinvent Business, Work and Life
Cover unavailable
68Compassionate 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.
69Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it's possible to teach a machine to excel at human endeavors--such as drawing, composing music, and completing tasks--by generating an understanding of how its actions affect its environment.
With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You'll also learn how to apply the techniques to your own datasets.
David Foster, cofounder of Applied Data Science, demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to the most cutting-edge algorithms in the field. Through tips and tricks, you'll learn how to make your models learn more efficiently and become more creative.
Get a fundamental overview of deep learning
Learn about libraries such as Keras and TensorFlow
Discover how variational autoencoders work
Get practical examples of generative adversarial networks (GANs)
Understand how autoregressive generative models function
Apply generative models within a reinforcement learning setting to accomplish tasks
With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You'll also learn how to apply the techniques to your own datasets.
David Foster, cofounder of Applied Data Science, demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to the most cutting-edge algorithms in the field. Through tips and tricks, you'll learn how to make your models learn more efficiently and become more creative.
Get a fundamental overview of deep learning
Learn about libraries such as Keras and TensorFlow
Discover how variational autoencoders work
Get practical examples of generative adversarial networks (GANs)
Understand how autoregressive generative models function
Apply generative models within a reinforcement learning setting to accomplish tasks
Cover unavailable
70Neural 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]
Cover unavailable
71Deep 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.
72Math for Deep Learning: What You Need to Know to Understand Neural Networks
Cover unavailable
73Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition
Cover unavailable
74Make 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.
Cover unavailable
75Neural Networks, Fuzzy Logic And Genetic Algorithms: Synthesis And Applications
Cover unavailable
76Natural 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.
Cover unavailable
77Fundamentals 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.
78Super Study Guide: Transformers & Large Language Models
Cover unavailable
79Machine Learning: An Applied Mathematics Introduction
Cover unavailable
80Neural 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.
Cover unavailable
81Hands-On Deep Learning with Go: A practical guide to building and implementing neural network models using Go
Cover unavailable
82Programming: 4 Manuscripts in 1 book: Python For Beginners - Python 3 Guide - Learn Java - Excel 2016
Cover unavailable
83Machine 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
Cover unavailable
84Deep 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
85#1 AI FOR BEGINNERS GUIDE: AI Made Simple: Your Step-by-Step Guide to Understanding, Using, and Thriving with Artificial Intelligence in Everyday Life and Beyond
Are you curious about how artificial intelligence is transforming everyday life, but feel overwhelmed by complex jargon and technical details? Do you wonder how AI might influence your future career or whether there's a place for you in the AI revolution?You're not alone if you answered "yes" to any of these questions.Teens and adults everywhere are eager to understand AI's impact, worried about its ethical implications, and excited to explore its potential in shaping our world.Luckily, you don’t have to be a tech whiz to grasp the power of AI!This engaging guide simplifies AI concepts, making them accessible to everyone and focusing on practical, everyday applications. Whether you're a tech-savvy teen or a beginner adult, this book provides the tools you need to thrive with AI.Here's just a glimpse of what you'll find inside:The 7 key principles of AI that everyone should know and how they are applied in everyday technologyThe top 10 AI advancements you should be aware of if you want to stay informed about the future4 ways AI is already revolutionizing industries and what that means for your career prospectsAn overview of ethical challenges that AI poses and how they're being addressed globallyThe secret behind how AI learns and makes decisions, making it easier to understand these complex processesWays to safeguard your personal information while living an AI-driven lifestyleThe most exciting uses of AI in solving global issues like climate change and moreA plain English breakdown of AI terminology and concepts—no computer science degree required6 interactive exercises to solidify your understanding and test your knowledge in engaging waysFuture predictions that explore what AI might mean for society and potential job marketsPractical tips on how AI can improve your daily life, from health to hobbies5 must-know skills to future-proof your career and win in the AI era...and so much more!
Cover unavailable
86Deep 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
Cover unavailable
87Augmented 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
Cover unavailable
88Predictive 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
Cover unavailable
89Studies 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.
Cover unavailable
90Deep Learning
Cover unavailable
91The 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.
Cover unavailable
92Neural 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.
93What Is ChatGPT Doing ... and Why Does It Work?
Cover unavailable
94Neural 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.
Cover unavailable
95Neural Networks for Beginners: An Easy Textbook for Machine Learning Fundamentals to Guide You Implementing Neural Networks with Python and Deep Learning (Artificial Intelligence)
Cover unavailable
96A 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.
Cover unavailable
97Computational 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.
Cover unavailable
98Pattern 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.
Cover unavailable
99Deep Learning and the Game of Go
Cover unavailable
100An 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.