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Books
Artificial Intelligence
HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Lowest new price: $26.00
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List price: $49.99
Author: Aurélien Géron
Brand: O Reilly Media

Graphics in this book are printed in black and white. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two productionready Python frameworks—scikitlearn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.  Explore the machine learning landscape, particularly neural nets
 Use scikitlearn to track an example machinelearning project endtoend
 Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
 Use the TensorFlow library to build and train neural nets
 Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
 Learn techniques for training and scaling deep neural nets
 Apply practical code examples without acquiring excessive machine learning theory or algorithm details
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Deep Learning (Adaptive Computation and Machine Learning series)
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List price: $80.00
Author: Ian Goodfellow
Brand: Goodfellow Ian

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. "Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."  Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
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 HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
 The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
 Python Machine Learning: Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow, 2nd Edition
 Pattern Recognition and Machine Learning (Information Science and Statistics)
 Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
 Deep Learning with Python
 Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
 An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
 Deep Learning: A Practitioner's Approach
 Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)


An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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Author: Gareth James

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, treebased methods, support vector machines, clustering, and more. Color graphics and realworld examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors cowrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and nonstatisticians alike who wish to use cuttingedge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
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 The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
 Applied Predictive Modeling
 R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
 Deep Learning (Adaptive Computation and Machine Learning series)
 HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
 The Art of R Programming: A Tour of Statistical Software Design
 Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (MIT Press)
 Machine Learning with R  Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems
 Accounting Theory: Conceptual Issues in a Political and Economic Environment
 Practical Statistics for Data Scientists: 50 Essential Concepts


Deep Learning with Python
Lowest new price: $47.49
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List price: $49.99
Author: Francois Chollet

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from nearunusable speech and image recognition, to nearhuman accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, naturallanguage processing, and generative models. By the time you finish, you'll have the knowledge and handson skills to apply deep learning in your own projects. What's Inside  Deep learning from first principles
 Setting up your own deeplearning environment
 Imageclassification models
 Deep learning for text and sequences
 Neural style transfer, text generation, and image generation
About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deeplearning library, as well as a contributor to the TensorFlow machinelearning framework. He also does deeplearning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1  FUNDAMENTALS OF DEEP LEARNING  What is deep learning?
 Before we begin: the mathematical building blocks of neural networks
 Getting started with neural networks
 Fundamentals of machine learning
PART 2  DEEP LEARNING IN PRACTICE Deep learning for computer vision
 Deep learning for text and sequences
 Advanced deeplearning best practices
 Generative deep learning
 Conclusions
 appendix A  Installing Keras and its dependencies on Ubuntu
 appendix B  Running Jupyter notebooks on an EC2 GPU instance
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Life 3.0: Being Human in the Age of Artificial Intelligence
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List price: $28.00
Author: Max Tegmark

New York Times Best Seller
How will Artificial Intelligence affect crime, war, justice, jobs, society and our very sense of being human? The rise of AI has the potential to transform our future more than any other technology—and there’s nobody better qualified or situated to explore that future than Max Tegmark, an MIT professor who’s helped mainstream research on how to keep AI beneficial. How can we grow our prosperity through automation without leaving people lacking income or purpose? What career advice should we give today’s kids? How can we make future AI systems more robust, so that they do what we want without crashing, malfunctioning or getting hacked? Should we fear an arms race in lethal autonomous weapons? Will machines eventually outsmart us at all tasks, replacing humans on the job market and perhaps altogether? Will AI help life flourish like never before or give us more power than we can handle? What sort of future do you want? This book empowers you to join what may be the most important conversation of our time. It doesn’t shy away from the full range of viewpoints or from the most controversial issues—from superintelligence to meaning, consciousness and the ultimate physical limits on life in the cosmos.
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Design Patterns: Elements of Reusable ObjectOriented Software
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Author: Erich Gamma
Brand: Erich Gamma

* Capturing a wealth of experience about the design of objectoriented software, four topnotch designers present a catalog of simple and succinct solutions to commonly occurring design problems. Previously undocumented, these 23 patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves. * The authors begin by describing what patterns are and how they can help you design objectoriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in objectoriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Lowest new price: $58.77
Lowest used price: $44.01
List price: $89.95
Author: Trevor Hastie

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boostingthe first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, nonnegative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.
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 This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry.
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Superintelligence: Paths, Dangers, Strategies
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List price: $15.95
Author: Nick Bostrom
Brand: Bostrom Nick

A New York Times bestseller
Superintelligence asks the questions: What happens when machines surpass humans in general intelligence? Will artificial agents save or destroy us? Nick Bostrom lays the foundation for understanding the future of humanity and intelligent life.
The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. If machine brains surpassed human brains in general intelligence, then this new superintelligence could become extremely powerful  possibly beyond our control. As the fate of the gorillas now depends more on humans than on the species itself, so would the fate of humankind depend on the actions of the machine superintelligence.
But we have one advantage: we get to make the first move. Will it be possible to construct a seed Artificial Intelligence, to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation?
This profoundly ambitious and original book breaks down a vast track of difficult intellectual terrain. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.
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 Superintelligence Paths Dangers Strategies
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Artificial Intelligence: A Modern Approach (3rd Edition)
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Lowest used price: $60.00
List price: $199.00
Author: Stuart Russell
Brand: Stuart Russell

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, uptodate introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or twosemester, undergraduate or graduatelevel courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times , the course on artificial intelligence is “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. To learn more about the course on artificial intelligence, visit http://www.aiclass.com. To read the full New York Times article, click here.
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Grokking Algorithms: An illustrated guide for programmers and other curious people
Lowest new price: $18.27
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List price: $44.99
Author: Aditya Bhargava
Brand: Bhargava Aditya

Summary Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. Learning about algorithms doesn't have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you'll find in Grokking Algorithms on Manning Publications' YouTube channel. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology An algorithm is nothing more than a stepbystep procedure for solving a problem. The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you. This fully illustrated and engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs. About the Book Grokking Algorithms is a friendly take on this core computer science topic. In it, you'll learn how to apply common algorithms to the practical programming problems you face every day. You'll start with tasks like sorting and searching. As you build up your skills, you'll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them. What's Inside  Covers search, sort, and graph algorithms
 Over 400 pictures with detailed walkthroughs
 Performance tradeoffs between algorithms
 Pythonbased code samples
About the Reader This easytoread, pictureheavy introduction is suitable for selftaught programmers, engineers, or anyone who wants to brush up on algorithms. About the Author Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io. Table of Contents  Introduction to algorithms
 Selection sort
 Recursion
 Quicksort
 Hash tables
 Breadthfirst search
 Dijkstra's algorithm
 Greedy algorithms
 Dynamic programming
 Knearest neighbors
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 Grokking Algorithms An Illustrated Guide for Programmers and Other Curious People
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