Machine learning Books
Penguin Books Ltd HOW WE LEARN
Book Synopsis'Absorbing, mind-enlarging, studded with insights ... This could have significant real-world results' Sunday Times Humanity's greatest feat is our incredible ability to learn. Even in their first year, infants acquire language, visual and social knowledge at a rate that surpasses the best supercomputers. But how, exactly, do our brains learn? In How We Learn, leading neuroscientist Stanislas Dehaene delves into the psychological, neuronal, synaptic and molecular mechanisms of learning. Drawing on case studies of children who learned despite huge difficulty and trauma, he explains why youth is such a sensitive period, during which brain plasticity is maximal, but also assures us that our abilities continue into adulthood. We can all enhance our learning and memory at any age and 'learn to learn' by taking maximal advantage of the four pillars of the brain's learning algorithm: attention, active engagement, error feedback and consolidation. The human braiTrade ReviewThis is an absorbing, mind-enlarging book, studded with insights ... Could have significant real-world results. -- James McConnachie * Sunday Times *An entertaining survey of how science from brain scans to psychological tests is helping inspire pedagogy. Dehaene challenges many tropes [and] describes much of his own pioneering work ... Well translated from the French with some touching references to his upbringing, from the cult film La Jetée to the writing of Daniel Pennac. -- Andrew Jack * Financial Times *An expert overview of learning ... Dehaene's fourth insightful exploration of neuroscience will pay dividends for attentive readers. * Kirkus *
£10.44
Oxford University Press Superintelligence
Book SynopsisThis seminal book injects the topic of superintelligence into the academic and popular mainstream. What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? In a tour de force of analytic thinking, Bostrom lays a foundation for understanding the future of humanity and intelligent life.Trade ReviewWorth reading. * Elon Musk, Founder of SpaceX and Tesla *I highly recommend this book * Bill Gates *very deep ... every paragraph has like six ideas embedded within it. * Nate Silver *Nick Bostrom makes a persuasive case that the future impact of AI is perhaps the most important issue the human race has ever faced. Instead of passively drifting, we need to steer a course. Superintelligence charts the submerged rocks of the future with unprecedented detail. It marks the beginning of a new era * Stuart Russell, Professor of Computer Science, University of California, Berkley *Those disposed to dismiss an 'AI takeover' as science fiction may think again after reading this original and well-argued book * Martin Rees, Past President, Royal Society *This superb analysis by one of the worlds clearest thinkers tackles one of humanitys greatest challenges: if future superhuman artificial intelligence becomes the biggest event in human history, then how can we ensure that it doesnt become the last? * Max Tegmark, Professor of Physics, MIT *Terribly important ... groundbreaking... extraordinary sagacity and clarity, enabling him to combine his wide-ranging knowledge over an impressively broad spectrum of disciplines - engineering, natural sciences, medicine, social sciences and philosophy - into a comprehensible whole... If this book gets the reception that it deserves, it may turn out the most important alarm bell since Rachel Carson's Silent Spring from 1962, or ever * Olle Haggstrom, Professor of Mathematical Statistics *Valuable. The implications of introducing a second intelligent species onto Earth are far-reaching enough to deserve hard thinking * The Economist *There is no doubting the force of [Bostrom's] arguments the problem is a research challenge worthy of the next generations best mathematical talent. Human civilisation is at stake * Financial Times *His book Superintelligence: Paths, Dangers, Strategies became an improbable bestseller in 2014 * Alex Massie, Times (Scotland) *Ein Text so nüchtern und cool, so angstfrei und dadurch umso erregender, dass danach das, was bisher vor allem Filme durchgespielt haben, auf einmal höchst plausibel erscheint. A text so sober and cool, so fearless and thus all the more exciting that what has until now mostly been acted through in films, all of a sudden appears most plausible afterwards. (translated from German) * Georg Diez, DER SPIEGEL *Worth reading.... We need to be super careful with AI. Potentially more dangerous than nukes * Elon Musk, Founder of SpaceX and Tesla *A damn hard read * Sunday Telegraph *I recommend Superintelligence by Nick Bostrom as an excellent book on this topic * Jolyon Brown, Linux Format *Every intelligent person should read it. * Nils Nilsson, Artificial Intelligence Pioneer, Stanford University *An intriguing mix of analytic philosophy, computer science and cutting-edge science fiction, Nick Bostrom's Superintelligence is required reading for anyone seeking to make sense of the recent surge of interest in artificial intelligence (AI). * Colin Garvey, Icon *Table of ContentsPreface 1: Past Developments and Present Capabilities 2: Roads to Superintelligence 3: Forms of Superintelligence 4: Singularity Dynamics 5: Decisive Strategic Advantage 6: Intellectual Superpowers 7: The Superintelligent Will 8: Is the Default Outcome Doom? 9: The Control Problem 10: Oracles, Genies, Sovereigns, Tools 11: Multipolar Scenarios 12: Acquiring Values 13: Design Choices 14: The Strategic Picture 15: Nut-Cutting Time Afterword
£11.39
MIT Press Ltd Reinforcement Learning
Book Synopsis
£85.50
MIT Press Ltd Deep Learning
Book Synopsis
£85.50
MIT Press Ltd The Deep Learning Revolution
a huge range and FREE tracked UK delivery on ALL orders.
£20.80
Oxford University Press Superintelligence Paths Dangers Strategies
Book SynopsisThis seminal book injects the topic of superintelligence into the academic and popular mainstream. What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? In a tour de force of analytic thinking, Bostrom lays a foundation for understanding the future of humanity and intelligent life.Trade ReviewWorth reading. * Elon Musk, Founder of SpaceX and Tesla *I highly recommend this book * Bill Gates *very deep ... every paragraph has like six ideas embedded within it. * Nate Silver *Nick Bostrom makes a persuasive case that the future impact of AI is perhaps the most important issue the human race has ever faced. Instead of passively drifting, we need to steer a course. Superintelligence charts the submerged rocks of the future with unprecedented detail. It marks the beginning of a new era * Stuart Russell, Professor of Computer Science, University of California, Berkley *Those disposed to dismiss an 'AI takeover' as science fiction may think again after reading this original and well-argued book * Martin Rees, Past President, Royal Society *This superb analysis by one of the worlds clearest thinkers tackles one of humanitys greatest challenges: if future superhuman artificial intelligence becomes the biggest event in human history, then how can we ensure that it doesnt become the last? * Max Tegmark, Professor of Physics, MIT *Terribly important ... groundbreaking... extraordinary sagacity and clarity, enabling him to combine his wide-ranging knowledge over an impressively broad spectrum of disciplines - engineering, natural sciences, medicine, social sciences and philosophy - into a comprehensible whole... If this book gets the reception that it deserves, it may turn out the most important alarm bell since Rachel Carson's Silent Spring from 1962, or ever * Olle Haggstrom, Professor of Mathematical Statistics *Valuable. The implications of introducing a second intelligent species onto Earth are far-reaching enough to deserve hard thinking * The Economist *There is no doubting the force of [Bostroms] arguments the problem is a research challenge worthy of the next generations best mathematical talent. Human civilisation is at stake * Financial Times *His book Superintelligence: Paths, Dangers, Strategies became an improbable bestseller in 2014 * Alex Massie, Times (Scotland) *Ein Text so nüchtern und cool, so angstfrei und dadurch umso erregender, dass danach das, was bisher vor allem Filme durchgespielt haben, auf einmal höchst plausibel erscheint. A text so sober and cool, so fearless and thus all the more exciting that what has until now mostly been acted through in films, all of a sudden appears most plausible afterwards. (translated from German) * Georg Diez, DER SPIEGEL *Worth reading.... We need to be super careful with AI. Potentially more dangerous than nukes * Elon Musk, Founder of SpaceX and Tesla *A damn hard read * Sunday Telegraph *I recommend Superintelligence by Nick Bostrom as an excellent book on this topic * Jolyon Brown, Linux Format *Every intelligent person should read it. * Nils Nilsson, Artificial Intelligence Pioneer, Stanford University *An intriguing mix of analytic philosophy, computer science and cutting-edge science fiction, Nick Bostrom's Superintelligence is required reading for anyone seeking to make sense of the recent surge of interest in artificial intelligence (AI). * Colin Garvey, Icon *Table of ContentsPreface ; 1. Past Developments and Present Capabilities ; 2. Roads to Superintelligence ; 3. Forms of Superintelligence ; 4. Singularity Dynamics ; 5. Decisive Strategic Advantage ; 6. Intellectual Superpowers ; 7. The Superintelligent Will ; 8. Is the Default Outcome Doom? ; 9. The Control Problem ; 10. Oracles, Genies, Sovereigns, Tools ; 11. Multipolar Scenarios ; 12. Acquiring Values ; 13. Design Choices ; 14. The Strategic Picture ; 15. Nut-Cutting Time
£20.24
HarperCollins Publishers A Brief History of Intelligence
Book SynopsisBridges the gap between AI and neuroscience by telling the story of how the brain came to be.''I found this book amazing'' Daniel Kahneman, Winner of the Nobel Prize in Economics and bestselling author of Thinking Fast & SlowThe entirety of the human brain's 4-billion-year story can be summarised as the culmination of five evolutionary breakthroughs, starting from the very first brains, all the way to the modern human brains. Each breakthrough emerged from new sets of brain modifications, and equipped animals with a new suite of intellectual faculties.These five breakthroughs are the organising map to this book, and they make up our itinerary for our adventure back in time. Each breakthrough also has fascinating corollaries to breakthroughs in AI. Indeed, there will be plenty of such surprises along the way. For instance: the innovation that enabled AI to beat humans in the game of Go temporal difference reinforcement learning was an innovation discovered by our fish ancestors over 500 million years ago. The solutions to many of the current mysteries in AI such as common sense' can be found in the tiny brain of a mouse. Where do emotions come from? Research suggests that they may have arisen simply as a solution to navigation in ancient worm brains. Unravelling this evolutionary story will reveal the hidden features of human intelligence and with them, just how your mind came to be.Trade Review‘Max Bennett published two scientific papers on brain evolution that blew me away. Now he has turned these into a fabulous book, A Brief History of Intelligence. His friendly writing style, clear jargon-free prose, and well of information make this book a winner.’ Joseph LeDoux, Henry and Lucy Moses Professor of Neural Science & Psychology at NYU, bestselling author of Anxious and A Deep History of Ourselves ‘With a truly mindboggling scope, A Brief History of Intelligence integrates the most relevant scientific knowledge to paint the big picture of how the human mind emerged. The red line through this book never gets blurred by unnecessary detail or jargoned language. It makes for exciting reading for virtually everybody, laypersons and experts alike.’ Kurt Kotrschal, Professor at Department of Behavioral Biology at University of Vienna, author of Dog & Human: The secret of our soul mates ‘If you’re in the least bit curious about that 3-pound grey blob between your ears, read this book. Max Bennett’s entertaining and enlightening natural history of brains is a tour de force–as refreshing as it is entertaining. It made my brain happy.’ Jonathan Balcombe, bestselling author of Super Fly, and the New York Times bestseller What a Fish Knows ‘Max Bennett gives a lively account of how brains evolved, and how the brain works today. A Brief History of Intelligence is engaging, comprehensive, and brimming with novel insights.’ Kent Berridge, James Olds Distinguished Professor of Psychology & Neuroscience at University of Michigan ‘If you want to know how our ancestors were able to “weaponize their imaginarium to survive”, then this is the book for you. In fact, this book discloses everything you always wanted to know about the brain (but were afraid to ask). It is an incredible resource.’ Karl Friston, Scientific Director for Wellcome Centre for Human Neuroimaging; Professor at Queen Square Institute of Neurology, University College London
£18.70
Cornerstone Artificial Intelligence (WIRED guides): How
Book SynopsisThe past decade has witnessed extraordinary advances in artificial intelligence. But what precisely is it and where does its future lie?In this brilliant, one-stop guide WIRED journalist Matt Burgess explains everything you need to know about AI. He describes how it works. He looks at the ways in which it has already brought us everything from voice recognition software to self-driving cars, and explores its potential for further revolutionary change in almost every area of our daily lives. He examines the darker side of machine learning: its susceptibility to hacking; its tendency to discriminate against particular groups; and its potential misuse by governments. And he addresses the fundamental question: can machines become as intelligent as human beings?Trade ReviewIn this book Burgess manages to cover all the key AI trends and developments over the last 60 years . . . delivers an informative and readable guide to all the main events that have taken place to date . . . We found this one helpful for a new or general reader and would recommend it to those looking for a good place to start in this field. * Irish Tech News *
£9.49
O'Reilly Media Designing Machine Learning Systems
Book SynopsisIn this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.
£39.74
O'Reilly Media GraphPowered Analytics and Machine Learning with
Book SynopsisThis practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.
£39.74
O'Reilly Media Embedded Analytics
Book SynopsisThe adoption of data analytics has remained remarkably static - perhaps reaching no more than thirty percent of potential users. This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations.
£35.99
O'Reilly Media Scaling Graph Learning for the Enterprise
Book Synopsis
£47.99
Manning Publications Machine Learning Algorithms in Depth
Book SynopsisDevelop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimisation for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimisation using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.
£54.89
Elsevier Science Data Mining
Book Synopsis
£54.86
HarperCollins Publishers How to Speak Whale
Book SynopsisFascinating' Greta ThunbergExtraordinary' Merlin SheldrakeA must-read' New ScientistEnthralling' George MonbiotBrilliant' Philip HoareWildlife filmmaker Tom Mustill had always liked whales. But when one breached onto his kayak, nearly killing him, he became obsessed.This book traces his extraordinary investigation into the deep ocean and the cutting-edge science of animal translation.What would it take to speak with a whale? Are we ready for what they might say?MORE PRAISE FOR HOW TO SPEAK WHALEOne of the most exciting and hopeful books I have read in ages' SY MONTGOMERY, AUTHOR OF THE SOUL OF AN OCTOPUSA narrative that will expand your concept of language and deepen your understanding of the many ways there are to be alive It left me inspired' MERLIN SHELDRAKE, AUTHOR OF ENTANGLED LIFEA must-read a hugely engaging personal story of a journey into the future of human-animal communication facilitated by delving into its past' NEW SCIENTISTFascinating and deeply humane' GRETA THUNBERGATrade Review‘A rich exploration of some of the world's most astonishing creatures … Mustill weaves a narrative that will expand your concept of language and deepen your understanding of the many ways there are to be alive. This is an extraordinary book that left me inspired’ Merlin Sheldrake, author of Entangled Life ‘A must-read… a hugely engaging personal story of a journey into the future of human-animal communication facilitated by delving into its past’ New Scientist ‘[An] extensively researched and energetic book… it is via the informed, far-reaching empathy of intermediaries such as Mustill that we stand our best chance of seeing into the non-human depths’ New Statesman ‘First-class … Reasoned, entertaining, and fact-filled’ Forbes ‘Fascinating and deeply humane’ Greta Thunberg ‘A rich, enthralling, brilliant book that opens our eyes and ears to worlds we can scarcely imagine’George Monbiot, Sunday Times bestselling author of Regenesis ‘Tantalizing … Think how transformative it would be if we could chat with whales about their love lives or their sorrows or their thoughts on the philosophy of language’ Elizabeth Kolbert, New Yorker ‘Mind-blowing … You will never feel closer to the magnificence of whales’Lucy Jones, author of Losing Eden ‘A scary, important and brilliant book … If we do get to translate ‘whale’, will we like what they’ve got to say?’Philip Hoare, author of Leviathan ‘Mustill takes us farther, much farther, than Dr. Dolittle ever imagined’ Carl Safina ‘Riveting … One of the most exciting and hopeful books I have read in ages’ Sy Montgomery, author of The Soul of an Octopus ‘Mustill conveys the richness of whale song and communication’ Frans de Waal ‘Lively and informative’ Jonathan Slaght, author of Owls of the Eastern Ice ‘Extraordinary’ Christiana Figueres
£10.44
O'Reilly Media Essential Math for Data Science
Book SynopsisTo succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.
£39.74
MIT Press Ltd Probabilistic Graphical Models
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£100.80
MIT Press Ltd Machine Learning
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£90.00
O'Reilly Media Deep Learning at Scale
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£47.99
BPB Publications Building Transformer Models with PyTorch 2.0
Book SynopsisThis book covers transformer architecture for various applications including NLP, computer vision, speech processing, and predictive modeling with tabular data. It is a valuable resource for anyone looking to harness the power of transformer architecture in their machine learning projects. The book provides a step-by-step guide to building transformer models from scratch and fine-tuning pre-trained open-source models. It explores foundational model architecture, including GPT, VIT, Whisper, TabTransformer, Stable Diffusion, and the core principles for solving various problems with transformers. The book also covers transfer learning, model training, and fine-tuning, and discusses how to utilize recent models from Hugging Face.
£26.59
MIT Press AI Ethics
Book Synopsis
£14.39
Manning Publications Deep Reinforcement Learning in Action
Book SynopsisHumans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Key features • Structuring problems as Markov Decision Processes • Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them • Applying reinforcement learning algorithms to real-world problems Audience You’ll need intermediate Python skills and a basic understanding of deep learning. About the technology Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but that’s not all it can do! Alexander Zai is a Machine Learning Engineer at Amazon AI working on MXNet that powers a suite of AWS machine learning products. Brandon Brown is a Machine Learning and Data Analysis blogger at outlace.com committed to providing clear teaching on difficult topics for newcomers.
£37.99
Manning Publications Grokking Deep Learning
Book SynopsisArtificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the “brain” behind the world’s smartest Artificial Intelligence systems out there. Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the “black box” API of some library or framework, readers will actually understand how to build these algorithms completely from scratch. Key Features:Build neural networks that can see and understand images Build an A.I. that will learn to defeat you in a classic Atari gameHands-on Learning Written for readers with high school-level math and intermediateprogramming skills. Experience with Calculus is helpful but notrequired. ABOUT THE TECHNOLOGY Deep Learning is a subset of Machine Learning, which is a field dedicated to the study and development of machines that can learn, often with the goal of eventually attaining general artificial intelligence.
£37.99
Manning Publications Math and Architectures of Deep Learning
Book SynopsisThe mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you'll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. about the technology It's important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems. about the book Math and Architectures of Deep Learning sets out the foundations of DL in a way that's both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you're done, you'll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge. Trade Review'This is a book that will reward your patience and perseverance with a clear and detailed knowledge of deep learning mathematics and associated techniques.' Tony Holdroyd 'Most online machine learning courses teach you how to get stuff done, but they don't give you the underlying math. If you want to know, this is the book for you!' Wiebe de Jong 'A really interesting book for people that want to understand the underlying mathematical mechanism of deep learning.' Julien Pohie 'Gives a unique perspective about machine learning and mathematical approaches.' Krzysztof Kamyczek 'An awesome book to get the grasp of the important mathematical skills to understand the very basics of deep learning.' Nicole KoenigsteinTable of Contentstable of contents READ IN LIVEBOOK 1AN OVERVIEW OF MACHINE LEARNING AND DEEP LEARNING READ IN LIVEBOOK 2INTRODUCTION TO VECTORS, MATRICES AND TENSORS FROM MACHINE LEARNING AND DATA SCIENCE POINT OF VIEW READ IN LIVEBOOK 3INTRODUCTION TO VECTOR CALCULUS FROM MACHINE LEARNING POINT OF VIEW READ IN LIVEBOOK 4LINEAR ALGEBRAIC TOOLS IN MACHINE LEARNING AND DATA SCIENCE READ IN LIVEBOOK 5PROBABILITY DISTRIBUTIONS FOR MACHINE LEARNING AND DATA SCIENCE READ IN LIVEBOOK 6BAYESIAN TOOLS FOR MACHINE LEARNING AND DATA SCIENCE READ IN LIVEBOOK 7FUNCTION APPROXIMATION: HOW NEURAL NETWORKS MODEL THE WORLD READ IN LIVEBOOK 8TRAINING NEURAL NETWORKS: FORWARD AND BACKPROPAGATION READ IN LIVEBOOK 9LOSS, OPTIMIZATION AND REGULARIZATION READ IN LIVEBOOK 10ONE, TWO AND THREE DIMENSIONAL CONVOLUTION AND TRANSPOSED CONVOLUTION IN NEURAL NETWORKS 11 IMAGE ANALYSIS: 2D CONVOLUTION BASED NEURAL NETWORK ARCHITECTURES FOR OBJECT RECOGNITION AND DETECTION 12 VIDEO ANALYSIS: 3D CONVOLUTION BASED SPATIO TEMPORAL NEURAL NETWORK ARCHITECTURES READ IN LIVEBOOK APPENDIX A: APPENDIX A.1Dot Product and cosine of the angle between two vectors A.2Computing variance of Gaussian Distribution A.3Two Theorems in Statistic
£47.47
Cambridge University Press FinTech Regulation in the United States
a huge range and FREE tracked UK delivery on ALL orders.
£18.00
Taylor & Francis Ltd Machine Learning for Business Analytics
Book SynopsisMachine Learning is an integral tool in a business analyst's arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing Table of Contents1. Introduction to Machine Learning for Data Analytics 2. Role of Machine Learning in Promoting Sustainability 3. Addressing the Utilization of Popular Regression Models in business applications 4. CHATBOTS: The Uses and Impact In The Hospitality Sector 5. Traversing Through the Use of Robotics in Medical Industry: Outlining Emerging Trends and Perspectives for Future Growth 6. Integration of AI in Insurance and Health Care: What Does It Mean? 7. Artificial Intelligence in Agriculture – A Review 8. Machine Learning and Artificial Intelligence-based Tools in Digital Marketing: An Integrated Approach 9. Application Of Artificial Intelligence In Market Knowledge And B2b Marketing Co-Creation 10 A Systematic Literature Review of Artificial Intelligence's Impact on Customer Experience 11. The Impact of Artificial Intelligence on Customer Experience and the Purchasing Process 12. Application of Artificial Intelligence in Banking – A Review 13. Digital Ethics: Towards a socially preferable development of AI systems
£47.49
Taylor & Francis Ltd Machine Learning for Managers
Book SynopsisMachine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations.Trade Review"If you are considering implementing machine learning in your business but don’t know where to start, this is the right book for you. Machine Learning for Managers is a comprehensive but non-technical introduction to the topic with many relevant examples and implementation guidelines. The split into a detailed overview and project management instructions is ideal for readers who don’t have the time to acquire programming skills but are passionate about leveraging AI to enhance business performance. The author’s very engaging writing style makes reading a book about a potentially very dry topic enjoyable."Christoph Schumacher, Professor of Innovation and Economics; Director Knowledge Exchange Hub, Massey University, New Zealand"This book fills an important gap between pure-technical and pure-managerial descriptions of machine learning (ML). Written in a no-nonsense light-hearted style, it is easy to follow, yet doesn’t shy away from using technical terms that are important for managers to be able to speak to their ML engineers. Highly recommended for managers looking to understand more about what is under the hood of ML."Tava Olsen, Professor, Deputy Dean, Melbourne Business School"Machine Learning for Managers is a safe haven for non-technical readers interested in understanding what AI and specifically ML is about. With clear, direct and witty language, Geertsema ensures that our journey into AI is like a walk in the park. It is easy, pleasurable and refreshing in its approach and powerful in its choice of illustrations. It brings to the forefront key concepts such as explainability, governance and business case making the message lucent and highly applicable to managers interested in incorporating ML into their business. As a practitioner focussed on human centric AI, I am particularly keen in bringing down AI/ML from its ivory tower status. This book is exactly a tool for this as it provides transparency, deciphers otherwise perceived complex language and is the basis for what ML should do best: to serve you. By far the best introductory ML roadmap I have come across. A must read."Jose Romano, Senior Manager at the European Investment Fund and former Entrepreneur in Residence at TAZI.AI"The two complementary parts of the book form a comprehensive and practical guide to machine learning. The first part provides a nontechnical overview of machine learning algorithms, demystifying the jargon in the field, which is crucial for students, lecturers and practitioners aiming to apply machine learning to resolve real-life business problems. The second part insightfully examines how machine learning outcomes can be developed and deployed in the organisation's processes. A recommended work for anyone looking to successfully manage the tsunami of big data!"Leo Paas, Professor, The University of Auckland Business School; Program Director, Master of Business Analytics"This book provides an outstanding introduction to machine learning from a management perspective. It gives a very clear presentation of the state-of-the-art machine learning methods and how to manage machine learning projects efficiently. It brings a fresh, unique focus on how to learn machine learning from a business perspective. It is highly practical and discusses in detail how a machine learning project should be deployed in real business applications. Not to be missed by any manager with a serious interest in AI and Machine Learning."Albert Bifet, Professor, Director of the AI Institute, The University of Waikato, New ZealandTable of ContentsPart 1: Understanding Machine Learning 1. Let's jump right in 2. Different kinds of ML 3. Creating ML models 4. Linear models 5. Neural networks 6. Tree-based approaches, ensembles and boosting 7. Dimensionality reduction and clustering 8. Unstructured data 9. Explainable AI Part 2: Managing Machine Learning Projects 10. The ML system lifecycle 11. The big picture 12. Creating value with ML 13. Making the business case 14. The ML pipeline 15. Development 16. Deployment and monitoring
£29.99
Taylor & Francis Ltd Deep LearningBased Forward Modeling and Inversion
Book SynopsisThis book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of thTable of Contents1. Deep Learning Framework and Paradigm in Computational Physics 2. Application of U-net in 3D Steady Heat Conduction Solver 3. Inversion of complex surface heat flux based on ConvLSTM 4. Time-domain electromagnetic inverse scattering based on deep learning 5. Reconstruction of thermophysical parameters based on deep learning 6. Advanced Deep Learning Techniques in Computational Physics
£77.99
O'Reilly Media Learning GitHub Copilot
Book Synopsis
£41.99
O'Reilly Media AI Value Creators
Book Synopsis
£62.25
Cambridge University Press The Statistical Physics of Data Assimilation and
Book SynopsisData assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.Table of Contents1. Prologue: linking 'The Future' with the present; 2. A data assimilation reminder; 3. Remembrance of things path; 4. SDA variational principles; Euler–Lagrange equations and Hamiltonian formulation; 5. Using waveform information; 6. Annealing in the model precision Rf; 7. Discrete time integration in data assimilation variational principles; Lagrangian and Hamiltonian formulations; 8. Monte Carlo methods; 9. Machine learning and its equivalence to statistical data assimilation; 10. Two examples of the practical use of data assimilation; 11. Unfinished business; Bibliography; Index.
£55.09
Harvard Business Review Press HBR's 10 Must Reads on AI
Book SynopsisThe next generation of AI is here—use it to lead your business forward.If you read nothing else on artificial intelligence and machine learning, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand the future direction of AI, bring your AI initiatives to scale, and use AI to transform your organization.This book will inspire you to: Create a new AI strategy Learn to work with intelligent robots Get more from your marketing AI Be ready for ethical and regulatory challenges Understand how generative AI is game changing Stop tinkering with AI and go all in This collection of articles includes "Competing in the Age of AI," by Marco Iansiti and Karim R. Lakhani; "How to Win with Machine Learning," by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; "Developing a Digital Mindset," by Tsedal Neeley and Paul Leonardi; "Learning to Work with Intelligent Machines," by Matt Beane; "Getting AI to Scale," by Tim Fountaine, Brian McCarthy, and Tamim Saleh; "Why You Aren't Getting More from Your Marketing AI," by Eva Ascarza, Michael Ross, and Bruce G. S. Hardie; "The Pitfalls of Pricing Algorithms," by Marco Bertini and Oded Koenigsberg; "A Smarter Strategy for Using Robots," by Ben Armstrong and Julie Shah; "Why You Need an AI Ethics Committee," by Reid Blackman; "Robots Need Us More Than We Need Them," by H. James Wilson and Paul R. Daugherty; "Stop Tinkering with AI," by Thomas H. Davenport and Nitin Mittal; and "ChatGPT Is a Tipping Point for AI," by Ethan Mollick.HBR's 10 Must Reads paperback series is the definitive collection of books for new and experienced leaders alike. Leaders looking for the inspiration that big ideas provide, both to accelerate their own growth and that of their companies, should look no further. HBR's 10 Must Reads series focuses on the core topics that every ambitious manager needs to know: leadership, strategy, change, managing people, and managing yourself. Harvard Business Review has sorted through hundreds of articles and selected only the most essential reading on each topic. Each title includes timeless advice that will be relevant regardless of an ever‐changing business environment.
£16.14
BCS Learning & Development Limited Artificial Intelligence Foundations: Learning
Book SynopsisIn alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today to support products, services, science and engineering, and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed. You will delve into the theory behind AI and machine learning projects, examining techniques for learning from data, the use of neural networks and why algorithms are so important in the development of a new AI agent or system.Trade Review'This book is a welcome read for those who want to know more about AI but don’t have a degree level background in the underlying subjects. It successfully brings the beginner up to speed and is written with such enthusiasm that the more complex topics become manageable. For a foundation level book it covers an impressive content range, whilst also providing a solid grounding in Machine Learning. The topics are clearly and succinctly exemplified by 3 case studies which bring AI into the real world.' -- Rosie Sheldon * Senior Trainer, TSG-training *'Many commentators see AI as the key enabler of the fifth industrial revolution. In this clear and concise guide to the BCS Artificial Intelligence Foundation qualification, Andrew Lowe and Steve Lawless set out to clearly explain the core concepts around artificial intelligence, machine learning, applications in robotics and more. A ‘must have’ guide in preparation for the BCS AI Foundation exam, this book will not only assist with exam success, but will capture your imagination to the possibilities in front of us when reimagining the human vs machine relationship.' -- Richard Webber * Digital Transformation Programme Manager, Ravensbourne University London *'This is a great book for executives to understand how AI may fit into their company and the types of benefits that they may gain and some of the pitfalls they may fall into. The book gives a clear picture of where AI fits with other types of decision-making processes. It covers many different aspects of how AI can be developed and how it affects society, with a particular focus on ethics and the human machine interface.' -- Dr Paul Edward Mort, B.Eng. (Hons), PhD, MBA, MIMechE CEng FNucI * Innovation Lead for Special Nuclear Materials, Sellafield Ltd *'Produces a great storm of ideas and thoughts with a funny touch from the authors...This is a piece of art and a “must read", for good immersion in the theme.' -- David Mondragón Tapia * IT & Business Consultant, DieresiS - Business and Professional Services *'This book is a highly recommended read for those starting out on Artificial Intelligence learning or as a refresher, written by two very knowledgeable authors, who put across the subject in an easy to understand manner.' -- Mat Gardam * Cyber Security Professional *'Manages to provide a fundamental grounding in the theory and application of AI without relying on mathematical or data science skills...the theory is colourfully illustrated with examples of both AI successes and challenges, which will prove an invaluable foundation to the reader's learning from experience in the world of Artificial Intelligence.' -- Mark Ainsworth * Director and Business Analyst, Promising ICT Limited *Table of Contents Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies
£33.24
Springer Nature Switzerland AG Deep Learning Architectures: A Mathematical
Book SynopsisThis book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter.This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject. Trade Review“This book is useful to students who have already had an introductory course in machine learning and are further interested to deepen their understanding of the machine learning material from the mathematical point of view.” (T. C. Mohan, zbMATH 1441.68001, 2020)Table of ContentsIntroductory Problems.- Activation Functions.- Cost Functions.- Finding Minima Algorithms.- Abstract Neurons.- Neural Networks.- Approximation Theorems.- Learning with One-dimensional Inputs.- Universal Approximators.- Exact Learning.- Information Representation.- Information Capacity Assessment.- Output Manifolds.- Neuromanifolds.- Pooling.- Convolutional Networks.- Recurrent Neural Networks.- Classification.- Generative Models.- Stochastic Networks.- Hints and Solutions.
£75.99
Springer Nature Switzerland AG Open Source Intelligence and Cyber Crime: Social
Book SynopsisThis book shows how open source intelligence can be a powerful tool for combating crime by linking local and global patterns to help understand how criminal activities are connected. Readers will encounter the latest advances in cutting-edge data mining, machine learning and predictive analytics combined with natural language processing and social network analysis to detect, disrupt, and neutralize cyber and physical threats. Chapters contain state-of-the-art social media analytics and open source intelligence research trends. This multidisciplinary volume will appeal to students, researchers, and professionals working in the fields of open source intelligence, cyber crime and social network analytics. Chapter Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.Table of ContentsChapter1. Studying the Weaponization of Social Media: Case Studies of Anti-NATO Disinformation Campaigns.- Chapter2. Cognitively-Inspired Inference for Malware Task Indentation.- Chapter3. Beyond the ‘Silk Road’: Assessing Illicit Drug Marketplaces on the Public Web.- Chapter4. Protecting the Web from Misinformation.- Chapter5. Social Media for Mental Health: Data, Methods, and Findings.- Chapter6. Twitter Bots and the Swedish Election.- Chapter7. Automated Text Analysis for Intelligence Purposes: A Psychological Operations Case Study.- Chapter8. You are Known by Your Friends: Leveraging Network Metrics for Bot Detection in Twitter.- Chapter9. Inferring Systemic Nets with Applications to Islamist Forums.
£89.99
Springer International Publishing AG System Design for Epidemics Using Machine
Book SynopsisThis book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.Table of Contents1. Pandemic effect of COVID-19: Identification, Present scenario and preventive measures using Machine learning model..- 2. A Comprehensive Review of the Smart Health Records to prevent Pandemic.- 3. Automation of COVID-19 Disease Diagnosis from Radiograph.- 4. Applications of Artificial Intelligence in the attainment of Sustainable Development Goals.- 5. A Novel Model for IoT Blockchain Assurance Based Compliance to COVID Quarantine.- 6. DEEP LEARNING BASED CONVOLUTIONALNEURAL NETWORK WITH RANDOM FOREST APPROACH FOR MRI BRAIN TUMOUR SEGMENTATION .- 7. Expert systems for improving the effectiveness of remote health monitoring in Covid-19 Pandemic - A Critical Review.- 8. Artificial Intelligence-based predictive tools for Life-threatening diseases.- 9. Deep Convolutional Generative Adversarial Network for Metastatic Tissue Diagnosis in Lymph Node Section.- 10. Transformation in Health Sector during Pandemic by Photonics Devices .- 11. DIAGNOSIS OF COVID-19 FROM CT IMAGES AND RESPIRATORY SOUND SIGNALS USING DEEP LEARNING STRATEGIES.- 12. The Role of Edge Computing in Pandemic and Epidemic Situations with its Solutions.- 13. Advances and application of Artificial Intelligence and Machine learning in the field of cardiovascular diseases and its role during the Pandemic condition.- 14. Effective Health Screening and Prompt Vaccination to Counter the Spread of Covid-19 and Minimize its Adverse Effects.- 15. CROWD DENSITY ESTIMATION USING NEURAL NETWORK FOR COVID’19 AND FUTURE PANDEMICS.- 16. “Role of digital healthcare in rehabilitation during pandemic”.- 17. AN EPIDEMIC OF NEURODEGENERATIVE DISEASE ANALYSIS USING MACHINE LEARNING TECHNIQUES.- 18. Covid-19 Growth Curve Forecasting for India using Deep Learning Techniques.
£142.49
De Gruyter Industrial Quantum Computing
Book Synopsis
£127.35
Springer International Publishing AG Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent
With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.
£80.99
World Scientific Publishing Co Pte Ltd Linear Algebra And Optimization With Applications
Book SynopsisThis book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
£81.00
Springer Verlag, Singapore Deep Reinforcement Learning
Book SynopsisDeep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.Table of ContentsContents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 What is Deep Reinforcement Learning? . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Three Machine Learning Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.3 Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 Tabular Value-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.1 Sequential Decision Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.2 Tabular Value-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.3 Classic Gym Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Approximating the Value Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.1 Large, High-Dimensional, Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663.2 Deep Value-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693.3 Atari 2600 Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874 Policy-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894.1 Continuous Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.2 Policy-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944.3 Locomotion and Visuo-Motor Environments . . . . . . . . . . . . . . . . . . . . 1114.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165 Model-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1195.1 Dynamics Models of High-Dimensional Problems . . . . . . . . . . . . . . . 1225.2 Learning and Planning Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1235.3 High-dimensional Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1365.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142viiviii CONTENTS5.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1446 Two-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1476.1 Two-Agent Zero-Sum Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1506.2 Tabula Rasa Self-Play Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1566.3 Self-Play Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1786.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1866.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1887 Multi-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1917.1 Multi-Agent Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1937.2 Multi-Agent Reinforcement Learning Agents . . . . . . . . . . . . . . . . . . . . 2027.3 Multi-Agent Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2147.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2217.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2238 Hierarchical Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 2258.1 Granularity of the Structure of Problems . . . . . . . . . . . . . . . . . . . . . . . 2278.2 Divide and Conquer for Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2298.3 Hierarchical Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2358.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2408.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2419 Meta Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2439.1 Learning to Learn Related Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2469.2 Transfer Learning and Meta Learning Agents . . . . . . . . . . . . . . . . . . . 2479.3 Meta-Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2619.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2679.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26810 Further Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27110.1 Developments in Deep Reinforcement Learning . . . . . . . . . . . . . . . . . 27110.2 Main Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27410.3 The Future of Articial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279A Deep Reinforcement Learning Suites . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283A.1 Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284A.2 Agent Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285A.3 Deep Learning Suites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286B Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287B.1 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287B.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294B.3 Datasets and Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311CONTENTS ixC Mathematical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.1 Sets and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.2 Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326C.3 Derivative of an Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334C.4 Bellman Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381x CONTENTSContents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 What is Deep Reinforcement Learning? . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1.2 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1.3 Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.1.5 Four Related Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.1.5.1 Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.1.5.2 Mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.1.5.3 Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.1.5.4 Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.2 Three Machine Learning Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.2.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141.2.2 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.2.3 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.3 Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.3.1 Prerequisite Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.3.2 Structure of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Tabular Value-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.1 Sequential Decision Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.2 Tabular Value-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.2.1 Agent and Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.2.2 Markov Decision Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.2.2.1 State ( . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292.2.2.2 Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.2.2.3 Transition )0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.2.2.4 Reward '0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.2.2.5 Discount Factor W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.2.2.6 Policy Function c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.2.3 MDP Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34xixii Contents2.2.3.1 Trace g . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352.2.3.2 State Value + . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362.2.3.3 State-Action Value & . . . . . . . . . . . . . . . . . . . . . . . . . . 372.2.3.4 Reinforcement Learning Objective . . . . . . . . . . . . . . 382.2.3.5 Bellman Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392.2.4 MDP Solution Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392.2.4.1 Hands On: Value Iteration in Gym . . . . . . . . . . . . . . . 412.2.4.2 Model-Free Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 442.2.4.3 Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472.2.4.4 O-Policy Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 492.2.4.5 Hands On: Q-learning on Taxi . . . . . . . . . . . . . . . . . . 522.3 Classic Gym Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.3.1 Mountain Car and Cartpole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.3.2 Path Planning and Board Games . . . . . . . . . . . . . . . . . . . . . . . . 562.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Approximating the Value Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.1 Large, High-Dimensional, Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663.1.1 Atari Arcade Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673.1.2 Real-Time Strategy and Video Games . . . . . . . . . . . . . . . . . . . . 683.2 Deep Value-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693.2.1 Generalization of Large Problem with Deep Learning . . . . . 693.2.1.1 Minimizing Supervised Target Loss . . . . . . . . . . . . . 693.2.1.2 Bootstrapping Q-Values . . . . . . . . . . . . . . . . . . . . . . . 703.2.1.3 Deep Reinforcement Learning Target-Error . . . . . 713.2.2 Three Problems: Coverage, Correlation, Convergence . . . . . 723.2.2.1 Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723.2.2.2 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723.2.2.3 Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733.2.3 Stable Deep Value-Based Learning . . . . . . . . . . . . . . . . . . . . . . 743.2.3.1 Decorrelating States . . . . . . . . . . . . . . . . . . . . . . . . . . . 753.2.3.2 Infrequent Updates of Target Weights . . . . . . . . . . . 763.2.3.3 Hands On: DQN and Breakout Gym Example . . . . . 763.2.4 Improving Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803.2.4.1 Overestimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 813.2.4.2 Distributional Methods . . . . . . . . . . . . . . . . . . . . . . . . 833.3 Atari 2600 Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833.3.1 Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843.3.2 Benchmarking Atari . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 853.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Contents xiii4 Policy-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894.1 Continuous Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.1.1 Continuous Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.1.2 Stochastic Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.1.3 Environments: Gym and MuJoCo . . . . . . . . . . . . . . . . . . . . . . . 924.1.3.1 Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924.1.3.2 Physics Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924.1.3.3 Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944.2 Policy-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944.2.1 Policy-Based Algorithm: REINFORCE . . . . . . . . . . . . . . . . . . . 954.2.2 Bias-Variance trade-o in Policy-Based Methods . . . . . . . . . 984.2.3 Actor Critic Bootstrapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994.2.4 Baseline Subtraction with Advantage Function . . . . . . . . . . . 1014.2.5 Trust Region Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1044.2.6 Entropy and Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1064.2.7 Deterministic Policy Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . 1074.2.8 Hands On: PPO and DDPG MuJoCo Examples . . . . . . . . . . . . . 1104.3 Locomotion and Visuo-Motor Environments . . . . . . . . . . . . . . . . . . . . 1114.3.1 Locomotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1114.3.2 Visuo-Motor Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1134.3.3 Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165 Model-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1195.1 Dynamics Models of High-Dimensional Problems . . . . . . . . . . . . . . . 1225.2 Learning and Planning Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1235.2.1 Learning the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1285.2.1.1 Modeling Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . 1285.2.1.2 Latent Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1295.2.2 Planning with the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1315.2.2.1 Trajectory Rollouts and Model-Predictive Control 1325.2.2.2 End-to-end Learning and Planning-by-Network . 1335.3 High-dimensional Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1365.3.1 Overview of Model-Based Experiments . . . . . . . . . . . . . . . . . . 1375.3.2 Small Navigation Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1385.3.3 Robotic Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1395.3.4 Games Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1395.3.5 Hands On: PlaNet Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1425.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144xiv Contents6 Two-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1476.1 Two-Agent Zero-Sum Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1506.1.1 The Diculty of Playing Go . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1526.1.2 AlphaGo Achievements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1556.2 Tabula Rasa Self-Play Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1566.2.1 Move-Level Self Play . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1606.2.1.1 Minimax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1616.2.1.2 Monte Carlo Tree Search . . . . . . . . . . . . . . . . . . . . . . 1646.2.2 Example-Level Self Play . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1716.2.2.1 Policy and Value Network . . . . . . . . . . . . . . . . . . . . . 1726.2.2.2 Stability and Exploration . . . . . . . . . . . . . . . . . . . . . . 1726.2.3 Tournament-Level Self Play . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1746.2.3.1 Self-Play Curriculum Learning . . . . . . . . . . . . . . . . . 1756.2.3.2 Supervised Curriculum Learning . . . . . . . . . . . . . . . 1756.3 Self-Play Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1786.3.1 How to Design a World Class Go Program? . . . . . . . . . . . . . . 1786.3.2 AlphaGo Zero Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1806.3.3 AlphaZero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1816.3.4 Open Self-Play Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1836.3.5 Hands On: Hex in Polygames Example . . . . . . . . . . . . . . . . . . . . 1846.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1866.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1887 Multi-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1917.1 Multi-Agent Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1937.1.1 Competitive Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1967.1.2 Cooperative Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1977.1.3 Mixed Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1987.1.4 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2007.1.4.1 Partial Observability . . . . . . . . . . . . . . . . . . . . . . . . . . 2017.1.4.2 Nonstationary Environments . . . . . . . . . . . . . . . . . . 2017.1.4.3 Large State Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2027.2 Multi-Agent Reinforcement Learning Agents . . . . . . . . . . . . . . . . . . . . 2027.2.1 Competitive Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2037.2.1.1 Counterfactual Regret Minimization . . . . . . . . . . . . 2037.2.1.2 Deep Counterfactual Regret Minimization . . . . . . . 2047.2.2 Cooperative Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2067.2.2.1 Centralized Training/Decentralized Execution . . . 2067.2.2.2 Opponent Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 2077.2.2.3 Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2087.2.2.4 Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2087.2.3 Mixed Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2097.2.3.1 Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . 2097.2.3.2 Swarm Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2117.2.3.3 Population-Based Training . . . . . . . . . . . . . . . . . . . . . 212Contents xv7.2.3.4 Self-Play Leagues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2137.3 Multi-Agent Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2147.3.1 Competitive Behavior: Poker . . . . . . . . . . . . . . . . . . . . . . . . . . . 2147.3.2 Cooperative Behavior: Hide and Seek. . . . . . . . . . . . . . . . . . . . 2167.3.3 Mixed Behavior: Capture the Flag and StarCraft . . . . . . . . . . 2187.3.4 Hands On: Hide and Seek in the Gym Example . . . . . . . . . . . . 2207.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2217.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2238 Hierarchical Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 2258.1 Granularity of the Structure of Problems . . . . . . . . . . . . . . . . . . . . . . . 2278.1.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2278.1.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2288.2 Divide and Conquer for Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2298.2.1 The Options Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2298.2.2 Finding Subgoals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2318.2.3 Overview of Hierarchical Algorithms . . . . . . . . . . . . . . . . . . . . 2318.2.3.1 Tabular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2328.2.3.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2328.3 Hierarchical Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2358.3.1 Four Rooms and Robot Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . 2358.3.2 Montezuma’s Revenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2368.3.3 Multi-Agent Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2388.3.4 Hands On: Hierarchical Actor Citic Example . . . . . . . . . . . . . . 2388.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2408.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2419 Meta Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2439.1 Learning to Learn Related Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2469.2 Transfer Learning and Meta Learning Agents . . . . . . . . . . . . . . . . . . . 2479.2.1 Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2489.2.1.1 Task Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2489.2.1.2 Pretraining and Finetuning . . . . . . . . . . . . . . . . . . . . 2499.2.1.3 Hands-on: Pretraining Example . . . . . . . . . . . . . . . . . 2499.2.1.4 Multi-task learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2509.2.1.5 Domain Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . 2519.2.2 Meta Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2539.2.2.1 Evaluating Few-Shot Learning Problems . . . . . . . . 2539.2.2.2 Deep Meta Learning Algorithms . . . . . . . . . . . . . . . 2549.2.2.3 Recurrent Meta Learning . . . . . . . . . . . . . . . . . . . . . . 2569.2.2.4 Model-Agnostic Meta Learning . . . . . . . . . . . . . . . . . 2579.2.2.5 Hyperparameter Optimization . . . . . . . . . . . . . . . . . 2599.2.2.6 Meta Learning and Curriculum Learning . . . . . . . . 2609.2.2.7 From Few-Shot to Zero-Shot Learning . . . . . . . . . . 2609.3 Meta-Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261xvi Contents9.3.1 Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2629.3.2 Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 2639.3.3 Meta Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2639.3.4 Meta World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2649.3.5 Alchemy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2659.3.6 Hands-on: Meta World Example . . . . . . . . . . . . . . . . . . . . . . . . . 2669.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2679.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26810 Further Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27110.1 Developments in Deep Reinforcement Learning . . . . . . . . . . . . . . . . . 27110.1.1 Tabular and Single-Agent Methods . . . . . . . . . . . . . . . . . . . . . . 27210.1.2 Deep Learning Model-Free Methods . . . . . . . . . . . . . . . . . . . . . 27210.1.3 Multi-Agent and Imperfect Information . . . . . . . . . . . . . . . . . . 27210.1.4 A Framework for Learning by Doing . . . . . . . . . . . . . . . . . . . . 27310.2 Main Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27410.2.1 Latent Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27510.2.2 Self Play . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27510.2.3 Hierarchical Reinforcement Learning . . . . . . . . . . . . . . . . . . . . 27510.2.4 Transfer Learning and Meta Learning . . . . . . . . . . . . . . . . . . . 27610.2.5 Population-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27610.2.6 Exploration and Intrinsic Motivation . . . . . . . . . . . . . . . . . . . . 27710.2.7 Explainable AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27810.2.8 Generalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27810.3 The Future of Articial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279A Deep Reinforcement Learning Suites . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283A.1 Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284A.2 Agent Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285A.3 Deep Learning Suites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286B Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287B.1 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287B.1.1 Training Set and Test Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288B.1.2 Curse of Dimensionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289B.1.3 Overtting and the Bias-Variance Trade-O . . . . . . . . . . . . . . 290B.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294B.2.1 Weights, Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294B.2.2 Backpropagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295B.2.3 End-to-end Feature Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 297B.2.4 Convolutional Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300B.2.5 Recurrent Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303B.2.6 More Network Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . 305B.2.7 Overtting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310B.3 Datasets and Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311Contents xviiB.3.1 Keras, TensorFlow, PyTorch . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312B.3.2 MNIST and ImageNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313B.3.3 GPU Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315B.3.4 Hands On: Classication Example . . . . . . . . . . . . . . . . . . . . . . . . 316B.3.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319C Mathematical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.1 Sets and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.1.1 Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.1.2 Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325C.2 Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326C.2.1 Discrete Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . 326C.2.2 Continuous Probability Distributions . . . . . . . . . . . . . . . . . . . . 327C.2.3 Conditional Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329C.2.4 Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330C.2.4.1 Expectation of a Random Variable . . . . . . . . . . . . . . 330C.2.4.2 Expectation of a Function of a Random Variable . 331C.2.5 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332C.2.5.1 Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332C.2.5.2 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332C.2.5.3 Cross-entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333C.2.5.4 Kullback-Leibler Divergence . . . . . . . . . . . . . . . . . . . 333C.3 Derivative of an Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334C.4 Bellman Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381
£42.74
APress Exploring the Power of ChatGPT
Book SynopsisLearn how to use the large-scale natural language processing model developed by OpenAI: ChatGPT. This book explains how ChatGPT uses machine learning to autonomously generate text based on user input and explores the significant implications for human communication and interaction. Author Eric Sarrion examines various aspects of ChatGPT, including its internal workings, use in computer projects, and impact on employment and society. He also addresses long-term perspectives for ChatGPT, including possible future advancements, adoption challenges, and considerations for ethical and responsible use. The book starts with an introduction to ChatGPT covering its versions, application areas, how it works with neural networks, NLP, and its advantages and limitations. Next, you'll be introduced to applications and training development projects using ChatGPT, as well as best practices for it. You'll then explore the ethical implications of ChatGPT, such as potentialbiases and risks, regulationTable of ContentsPart 1: Introduction to ChatGPT1 - What is ChatGPT ?Describes hat is ChatGPT, its history...• 1.1 Definition of ChatGPT• 1.2 ChatGPT History• 1.3 Versions of ChatGPT• 1.4 Application areas of ChatGPT2 - How Does ChatGPT Work?Describes how it works inside• 2.1 Neural networks• 2.2 Natural language processing techniques used by ChatGPT• 2.3 The data used to train ChatGPT• 2.4 The advantages and limitations of ChatGPT3 - Applications of ChatGPTDescribes what you can do whith ChatGPT• 3.1 Chatbots and virtual assistants• 3.2 Machine translation apps• 3.3 Content writing apps• 3.4 Applications in information retrievalPart 2: How To Train and Use ChatGPT4 - ChatGPT TrainingDescribes how to build the models used by ChatGPT• 4.1 Data collection and preparation• 4.2 ChatGPT training settings• 4.3 Training tools available• 4.4 Techniques to improve ChatGPT performance5 - Using ChatGPT in Development ProjectsDescribes how to use ChatGPT in a web page with an API• 5.1 Libraries and frameworks for ChatGPT• 5.2 Examples of projects using ChatGPT• 5.3 Techniques to integrate ChatGPT into applications• 5.4 Use ChatGPT with the OpenAI API• 5.5 Use ChatGPT with a voice interface• 5.6 Methods to evaluate the performance of ChatGPT6 - Best Practices for Using ChatGPTDescribes how to optimize ChatGPT• 6.1 Strategies to ensure the quality of input data• 6.2 Techniques to avoid bias in data• 6.3 Methods to optimize ChatGPT performance• 6.4 ChatGPT maintenance tipsPart 3 The Ethical Implications of ChatGPT7 - Potential Biases and Risks of ChatGPTDescribes biases and riks of ChatGPT• 7.1 Sources of bias in the data• 7.2 The risks of discrimination and stigmatization• 7.3 The limits of ChatGPT transparency• 7.4 Consequences for privacy and data security 8 - The Implications of ChatGPT on Employment and SocietyDescribes impacts on employment and society• 8.1 The impacts on employment in various sectors• 8.2 The implications for education and vocational training• 8.3 Consequences for social and cultural norms• 8.4 Political and legal responses to the changes brought about by ChatGPT9 - Regulations and Standards for Using ChatGPTDescribes responsible use of ChatGPT• 9.1 Existing regulations for consumer protection• 9.2 Standards for Responsible Use of ChatGPT• 9.3 ChatGPT governance initiatives• 9.4 Considerations for Legal and Ethical Responsibility of ChatGPTPart 4 Future Prospects of ChatGPT10 - Future Developments of ChatGPTDescribes future developments • 10.1 Advances in Machine Learning and Natural Language Processing Research• 10.2 ChatGPT performance and efficiency improvements• 10.3 Advances in applications and areas of use of ChatGPT• 10.4 Developments in the competition and the ChatGPT market11 - The Long Term Outlook for ChatGPTDescribes long term outlook• 11.1 The implications for artificial intelligence and cognition• 11.2 Merging possibilities between ChatGPT and other emerging technologies• 11.3 The challenges of adopting and accepting ChatGPT• 11.4 Issues for regulation and governance of ChatGPTPart 5 : Examples of Using ChatGPT12 - Using ChatGPT for Text Content Creation13 - Using ChatGPT for Software Programming14 - Using ChatGPT for Text Translation15 - Using ChatGPT for Artistic Content Creation16 - Using ChatGPT for Innovation and Creativity17 - ConclusionGives a conclusion of the book• 17.1 Summaries of the key elements covered in the book• 17.2 Final thoughts on the impact and implications of ChatGPT• 17.3 Suggestions for future research and development on ChatGPT• 17.4 Considerations for the ethical and responsible use of ChatGPT in the future.• 17.5 In conclusion
£24.74
John Murray Press Machines that Think: Everything you need to know
Book SynopsisSometime in the future the intelligence of machines will exceed that of human brain power. So are we on the edge of an AI-pocalypse, with superintelligent devices superseding humanity, as predicted by Stephen Hawking? Or will this herald a kind of Utopia, with machines doing a far better job at complex tasks than us? You might not realise it, but you interact with AIs every day. They route your phone calls, approve your credit card transactions and help your doctor interpret results. Driverless cars will soon be on the roads with a decision-making computer in charge. But how do machines actually think and learn? In Machines That Think, AI experts and New Scientist explore how artificial intelligence helps us understand human intelligence, machines that compose music and write stories - and ask if AI is really a threat.ABOUT THE SERIESNew Scientist Instant Expert books are definitive and accessible entry points to the most important subjects in science; subjects that challenge, attract debate, invite controversy and engage the most enquiring minds. Designed for curious readers who want to know how things work and why, the Instant Expert series explores the topics that really matter and their impact on individuals, society, and the planet, translating the scientific complexities around us into language that's open to everyone, and putting new ideas and discoveries into perspective and context.
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MIT Press Artificial Unintelligence How Computers
Book SynopsisA guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right.In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right.Making a case against technochauvinism—the belief that technology is always the solution̵
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Manning Publications Ensemble Methods for Machine Learning
Book SynopsisMany machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models. About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.Trade Review"The definitive and complete guide on ensemble learning. A must read!" Al Krinker "The examples are clear and easy to reproduce, the writing is engaging and clear, and the reader is not bogged down by details which might be unimportant for beginners in the field!" Or Golan "This book is a great tutorial on ensemble methods!" Stephen Warnett "The code examples as well as the case studies at the end of each chapter open many possibilities of using these techniques on your data/projects." Joaquin Beltran
£41.39
Manning Publications Machine Learning Engineering in Action
Book SynopsisMachine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You will adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code! You will learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code's architecture for improved resilience. You will even discover when not to use machine learning—and the alternative approaches that might be cheaper and more effective. When you're done working through this toolbox guide, you will be able to reliably deliver cost-effective solutions for organizations big and small alike. Following established processes and methodology maximizes the likelihood that your machine learning projects will survive and succeed for the long haul. By adopting standard, reproducible practices, your projects will be maintainable over time and easy for new team members to understand and adapt. Trade Review“Anice view on practical data science and machine learning. Great reading fornewbies, some interesting views for seasoned practitioners.” Johannes Verwijnen “Amust read for those looking to balance the planning and experimentationlifecycle.” Jesús Antonino Juárez Guerrero “Apractical book to help engineers understand the workflow of machine learningprojects.” Xiangbo Mao “Donot implement your ML model into production without reading this book!” Lokesh Kumar
£43.12
Manning Publications Evolutionary Deep Learning
Book SynopsisDiscover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning's common pitfalls and deliver adaptable model upgrades without constant manual adjustment. In Evolutionary Deep Learning you will learn how to: Solve complex design and analysis problems with evolutionary computation Tune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimization Use unsupervised learning with a deep learning autoencoder to regenerate sample data Understand the basics of reinforcement learning and the Q Learning equation Apply Q Learning to deep learning to produce deep reinforcement learning Optimize the loss function and network architecture of unsupervised autoencoders Make an evolutionary agent that can play an OpenAI Gym game Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. about the technology Evolutionary deep learning merges the biology-simulating practices of evolutionary computation (EC) with the neural networks of deep learning. This unique approach can automate entire DL systems and help uncover new strategies and architectures. It gives new and aspiring AI engineers a set of optimization tools that can reliably improve output without demanding an endless churn of new data. about the reader For data scientists who know Python.
£43.69
Manning Publications Deep Learning with Pytorch Second Edition
Book Synopsis Luca Antiga is a deep learning researcher and entrepreneur known for translating theory into high-impact AI applications. With extensive industry and academic collaborations, Luca brings clarity and pragmatic rigor to every page. He distills years of neural-network innovation into guidance that accelerates reader competence. Eli Stevens is a seasoned machine-learning engineer recognized for simplifying complex architectures for production teams. With startup intensity and open-source spirit, Eli delivers frank, actionable insights throughout the book. He converts frontier research into approachable steps that help readers deploy real-world solutions. Howard Huang is a software engineer on the core PyTorch team, known for advancing distributed training at scale. With insider knowledge of the framework, Howard injects authoritative best practices into the narrative. He turns deep infrastructure expertise into clear tactics that boost reader productivity. Thomas Viehmann is a data-science consultant and educator praised for demystifying advanced AI concepts. With classroom experience and community mentorship, Thomas offers an encouraging, structured teaching style. He translates academic depth into tools and patterns readers can apply immediately.
£43.20
No Starch Press,US The Art Of Machine Learning: A Hands-On Guide to
Book SynopsisMachine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language. You'll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbours method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You'll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you'll need in practice. Additional features: How to avoid common problems, suTrade Review"In contrast to other books about machine learning, there is a bigger emphasis on programming and usage in practice. In particular, there is an excellent explanation of how to avoid over/under-fitting, and how to use cross-validation. This book is sure to be helpful for students who are interested to understand the core concepts, as well as their practical implementations in R."—Toby Dylan Hocking, Assistant Professor, Northern Arizona University"The Art of Machine Learning by Norman Matloff is a welcome addition to a growing body of books about machine learning. Matloff, whose career spans both computer science and statistics, addresses the new and exciting field with a fresh approach."—Dirk Eddelbuettel, Department of Statistics, University of IllinoisTable of ContentsAcknowledgmentsIntroductionPART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODSChapter 1: Regression ModelsChapter 2: Classification ModelsChapter 3: Bias, Variance, Overfitting, and Cross-ValidationChapter 4: Dealing with Large Numbers of FeaturesPART II: TREE-BASED METHODSChapter 5: A Step Beyond k-NN: Decision TreesChapter 6: Tweaking the TreesChapter 7: Finding a Good Set of HyperparametersPART III: METHODS BASED ON LINEAR RELATIONSHIPSChapter 8: Parametric MethodsChapter 9: Cutting Things Down to Size: RegularizationPART IV: METHODS BASED ON SEPARATING LINES AND PLANESChapter 10: A Boundary Approach: Support Vector MachinesChapter 11: Linear Models on Steroids: Neural NetworksPART V: APPLICATIONSChapter 12: Image Classification Chapter 13: Handling Time Series and Text Data Appendix A: List of Acronyms and Symbols Appendix B: Statistics and ML Terminology CorrespondenceAppendix C: Matrices, Data Frames, and Factor ConversionsAppendix D: Pitfall: Beware of “p-Hacking”!
£35.99
