Would you like to learn the Best Sellers in AI & Machine Learning on Amazon as of now (31 December 2019)?
Machine learning is the concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence (AI) that keeps a computer’s built-in algorithms current regardless of changes in the worldwide economy. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
AI Superpowers: China, Silicon Valley, and the New World Order
Dr. Kai-Fu Lee—one of the world’s most respected experts on AI and China—reveals that China has suddenly caught up to the US at an astonishingly rapid and unexpected pace.
In AI Superpowers, Kai-fu Lee argues powerfully that because of these unprecedented developments in AI, dramatic changes will be happening much sooner than many of us expected. Indeed, as the US-Sino AI competition begins to heat up, Lee urges the US and China to both accept and to embrace the great responsibilities that come with significant technological power. Most experts already say that AI will have a devastating impact on blue-collar jobs. But Lee predicts that Chinese and American AI will have a strong impact on white-collar jobs as well. Is universal basic income the solution? In Lee’s opinion, probably not. But he provides a clear description of which jobs will be affected and how soon, which jobs can be enhanced with AI, and most importantly, how we can provide solutions to some of the most profound changes in human history that are coming soon.
The Singularity Is Near: When Humans Transcend Biolog
For over three decades, Ray Kurzweil has been one of the most respected and provocative advocates of the role of technology in our future. In his classic The Age of Spiritual Machines, he argued that computers would soon rival the full range of human intelligence at its best. Now he examines the next step in this inexorable evolutionary process: the union of human and machine, in which the knowledge and skills embedded in our brains will be combined with the vastly greater capacity, speed, and knowledge-sharing ability of our creations.
Gödel, Escher, Bach: An Eternal Golden Braid
Douglas Hofstadter’s book is concerned directly with the nature of “maps” or links between formal systems. However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it. If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence. Gödel, Escher, Bach is a wonderful exploration of fascinating ideas at the heart of cognitive science: meaning, reduction, recursion, and much more.
Machine Learning For Absolute Beginners: A Plain English Introduction
Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world 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.
You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place
Discover the book that Malcolm Gladwell, Susan Cain, Daniel Pink, and Adam Grant want you to read this year, an “accessible, informative, and hilarious” introduction to the weird and wonderful world of artificial intelligence (Ryan North).
“You look like a thing and I love you” is one of the best pickup lines ever… according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog AI Weirdness. She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans–all to understand the technology that governs so much of our daily lives.
Code: The Hidden Language of Computer Hardware and Software
What do flashlights, the British invasion, black cats, and seesaws have to do with computers? In CODE, they show us the ingenious ways we manipulate language and invent new means of communicating with each other. And through CODE, we see how this ingenuity and our very human compulsion to communicate have driven the technological innovations of the past two centuries.
Using everyday objects and familiar language systems such as Braille and Morse code, author Charles Petzold weaves an illuminating narrative for anyone who’s ever wondered about the secret inner life of computers and other smart machines.
It’s a cleverly illustrated and eminently comprehensible story—and along the way, you’ll discover you’ve gained a real context for understanding today’s world of PCs, digital media, and the Internet. No matter what your level of technical savvy, CODE will charm you—and perhaps even awaken the technophile within.
Applied Artificial Intelligence: A Handbook For Business Leaders
“Artificial intelligence” is the buzzword of the day. You’ve no doubt read your fair share of media hype either proclaiming doom and gloom where robots seize our jobs or prophesying a new utopia where AI cures all our human problems. But what does it actually mean for your role as a business leader?
Applied Artificial Intelligence is a practical guide for business leaders who are passionate about leveraging machine intelligence to enhance the productivity of their organizations and the quality of life in their communities. If you want to drive innovation by combining data, technology, design, and people to solve real problems at an enterprise scale, this is your playbook.
Superintelligence: Paths, Dangers, Strategies
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.
Deep Learning (Adaptive Computation and Machine Learning series)
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.
Falter: Has the Human Game Begun to Play Itself Out?
Thirty years ago Bill McKibben offered one of the earliest warnings about climate change. Now he broadens the warning: the entire human game, he suggests, has begun to play itself out.
Bill McKibben’s groundbreaking book The End of Nature — issued in dozens of languages and long regarded as a classic — was the first book to alert us to global warming. But the danger is broader than that: even as climate change shrinks the space where our civilization can exist, new technologies like artificial intelligence and robotics threaten to bleach away the variety of human experience.
The Hundred-Page Machine Learning Book
Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of the machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.”
The Book of Why: The New Science of Cause and Effect
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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 liberal use of color 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 boosting—the 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, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.
Deep Learning with Python
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, natural-language processing, and generative models. By the time you finish, you’ll have the knowledge and hands-on skills to apply deep learning in your own projects.
Digital Transformation: Survive and Thrive in an Era of Mass Extinction
From visionary Silicon Valley entrepreneur Tom Siebel comes a penetrating examination of the new technologies that are disrupting business and government—and how organizations can harness them to transform into digital enterprises.
The confluence of four technologies—elastic cloud computing, big data, artificial intelligence, and the internet of things —writes Siebel, is fundamentally changing how business and government will operate in the 21st century.
Siebel masterfully guides readers through a fascinating discussion of the game-changing technologies driving digital transformation and provides a roadmap to seize them as a strategic opportunity. He shows how leading enterprises such as Enel, 3M, Royal Dutch Shell, the U.S. Department of Defense, and others are applying AI and IoT with stunning results.
Digital Transformation is the guidebook every business and government leader needs to survive and thrive in the new digital age.
Advances in Financial Machine Learning
Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Life 3.0: Being Human in the Age of Artificial Intelligence
In this authoritative and eye-opening book, Max Tegmark describes and illuminates the recent, path-breaking advances in Artificial Intelligence and how it is poised to overtake human intelligence. How will AI 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.
Prediction Machines: The Simple Economics of Artificial Intelligence
Artificial intelligence does the seemingly impossible, magically bringing machines to life – driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.
But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policymakers, investors, and entrepreneurs.
When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity – operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete.
Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.
Design Patterns: Elements of Reusable Object-Oriented Software
Capturing a wealth of experience about the design of object-oriented software, four top-notch 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 object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented 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.
Each pattern describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design. All patterns are compiled from real systems and are based on real-world examples. Each pattern also includes code that demonstrates how it may be implemented in object-oriented programming languages like C++ or Smalltalk.
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
One of America’s top doctors reveals how AI will empower physicians and revolutionize patient care Medicine has become inhuman, to disastrous effect. The doctor-patient relationship–the heart of medicine–is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard.
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner–the Master Algorithm–and discusses what it will mean for business, science, and society. If data-ism is today’s philosophy, this book is its bible.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you’ll keep coming back to as you build your machine learning systems.
Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.
Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It’s also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.
This book is your companion to machine learning with Python, whether you’re a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
Artificial Intelligence: A Guide for Thinking Humans
No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals its turbulent history and the recent surge of apparent successes, grand hopes, and emerging fears that surround AI.
In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent – really – are the best AI programs? How do they work? What can they actually do, and when do they fail? How human-like do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant methods of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought that led to recent achievements. She meets with fellow experts like Douglas Hofstadter, the cognitive scientist and Pulitzer Prize-winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much farther it has to go.
Interweaving stories about the science and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and approachable accounts of the most interesting and provocative modern work in AI, flavored with Mitchell’s humor and personal observations. This frank, lively book will prove an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impacts on all of our futures.
Source : https://www.amazon.com/Best-Sellers-Books-AI-Machine-Learning/zgbs/books/3887