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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. "Written by three experts in the field, Deep Learning is the o...

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"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introducti...

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This 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 thinkin...

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Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next....

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'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times 'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year In this New York Times bestseller, Cathy O'Neil...

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Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now re...

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Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad...

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This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the mo...

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This 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 thinkin...

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This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, stron...

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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learni...

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The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the e...

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'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 k...

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Robots may one day rule the world, but what is a robot-ruled Earth like? Many think that the first truly smart robots will be brain emulations or "ems." Robin Hanson draws on decades of expertise in economics, physics, and computer science to pain...

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To 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...

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The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the previous editions. Completely redesigned and revised, it introduces the theoretical foundations of neuroscience with a focus on the nature of i...

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This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF form...

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If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemente...

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A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on ...

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The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intel...

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As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a ...

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The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. ...

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The 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....

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Herbert Simon's classic work on artificial intelligence in the expanded and updated third edition from 1996, with a new introduction by John E. Laird. Herbert Simon's classic and influential The Sciences of the Artificial declares definitively tha...

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We often hear that AI is revolutionising the financial sector, like no other technology has done before. This book looks beyond these clichs and explores all aspects of this transformation at a deep level. It spells out a vision for the future and...

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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparat...

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Simplify data science infrastructure to give data scientists an efficient path from prototype to production. Effective Data Science Infrastructure is a hands-on guide to assembling infrastructure for data science and machine learning applications....

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'A must-read' New Scientist 'Fascinating' Greta Thunberg 'Enthralling' George Monbiot 'Brilliant' Philip Hoare A thrilling investigation into the pioneering world of animal communication, where big data and artificial intelligence are changing our...

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This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data. The book has been upd...

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Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--G...

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