The Master Algorithm

The Master Algorithm
  • Author : Pedro Domingos
  • Publisher : Basic Books
  • Pages : 352
  • Relase : 2015-09-22
  • ISBN : 9780465061921

The Master Algorithm Book Review:

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

The Master Algorithm

The Master Algorithm
  • Author : Pedro Domingos
  • Publisher : Penguin UK
  • Pages : 352
  • Relase : 2015-09-22
  • ISBN : 9780241004555

The Master Algorithm Book Review:

A spell-binding quest for the one algorithm capable of deriving all knowledge from data, including a cure for cancer Society is changing, one learning algorithm at a time, from search engines to online dating, personalized medicine to predicting the stock market. But learning algorithms are not just about Big Data - these algorithms take raw data and make it useful by creating more algorithms. This is something new under the sun: a technology that builds itself. In The Master Algorithm, Pedro Domingos reveals how machine learning is remaking business, politics, science and war. And he takes us on an awe-inspiring quest to find 'The Master Algorithm' - a universal learner capable of deriving all knowledge from data.

The Master Algorithm

The Master Algorithm
  • Author : Pedro Domingos
  • Publisher : Hachette UK
  • Pages : 352
  • Relase : 2015-09-22
  • ISBN : 9780465061921

The Master Algorithm Book Review:

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

Machinehood

Machinehood
  • Author : S.B. Divya
  • Publisher : Simon and Schuster
  • Pages : 416
  • Relase : 2021-03-02
  • ISBN : 9781982148089

Machinehood Book Review:

Zero Dark Thirty meets The Social Network in this “clever…gritty” (Ken Liu, author of The Grace of Kings) science fiction thriller about artificial intelligence, sentience, and labor rights in a near future dominated by the gig economy—from Hugo Award nominee S.B. Divya. Welga Ramirez, executive bodyguard and ex-special forces, is about to retire early when her client is killed in front of her. It’s, 2095 and people don’t usually die from violence. Humanity is entirely dependent on pills that not only help them stay alive but allow them to compete with artificial intelligence in an increasingly competitive gig economy. Daily doses protect against designer diseases, flow enhances focus, zips and buffs enhance physical strength and speed, and juvers speed the healing process. All that changes when Welga’s client is killed by The Machinehood, a new and mysterious terrorist group that has simultaneously attacked several major pill funders. The Machinehood operatives seem to be part human, part machine, something the world has never seen. They issue an ultimatum: stop all pill production in one week. Global panic ensues as pill production slows and many become ill. Thousands destroy their bots in fear of a strong AI takeover. But the US government believes the Machinehood is a cover for an old enemy. One that Welga is uniquely qualified to fight. Welga, determined to take down the Machinehood, is pulled back into intelligence work by the government that betrayed her. But who are the Machinehood, and what do they really want? A “fantastic, big-idea thriller” (Malka Older, Hugo Award finalist for The Centenal Cycle series) that asks: if we won’t see machines as human, will we instead see humans as machines?

Probably Approximately Correct

Probably Approximately Correct
  • Author : Leslie Valiant
  • Publisher : Basic Books
  • Pages : 208
  • Relase : 2013-06-04
  • ISBN : 9780465037902

Probably Approximately Correct Book Review:

We have effective theories for very few things. Gravity is one, electromagnetism another. But for most things—whether as mundane as finding a mate or as major as managing an economy—our theories are lousy or nonexistent. Fortunately, we don't need them, any more than a fish needs a theory of water to swim; we're able to muddle through. But how do we do it? In Probably Approximately Correct, computer scientist Leslie Valiant presents a theory of the theoryless. The key is “probably approximately correct” learning, Valiant's model of how anything can act without needing to understand what is going on. The study of probably approximately correct algorithms reveals the shared computational nature of evolution and cognition, indicates how computers might possess authentic intelligence, and shows why hacking a problem can be far more effective than developing a theory to explain it. After all, finding a mate is a lot more satisfying than finding a theory of mating. Offering an elegant, powerful model that encompasses all of life's complexity, Probably Approximately Correct will revolutionize the way we look at the universe's greatest mysteries.

Master Machine Learning Algorithms

Master Machine Learning Algorithms
  • Author : Jason Brownlee
  • Publisher : Machine Learning Mastery
  • Pages : 163
  • Relase : 2016-03-04
  • ISBN :

Master Machine Learning Algorithms Book Review:

You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step.

The Ethical Algorithm

The Ethical Algorithm
  • Author : Michael Kearns,Aaron Roth
  • Publisher : Oxford University Press
  • Pages : 288
  • Relase : 2019-10-04
  • ISBN : 9780190948214

The Ethical Algorithm Book Review:

Over the course of a generation, algorithms have gone from mathematical abstractions to powerful mediators of daily life. Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps. Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

Reinforcement Learning, second edition

Reinforcement Learning, second edition
  • Author : Richard S. Sutton,Andrew G. Barto
  • Publisher : MIT Press
  • Pages : 552
  • Relase : 2018-11-13
  • ISBN : 9780262352703

Reinforcement Learning, second edition Book Review:

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

How Smart Machines Think

How Smart Machines Think
  • Author : Sean Gerrish
  • Publisher : MIT Press
  • Pages : 312
  • Relase : 2019-10-22
  • ISBN : 9780262537971

How Smart Machines Think Book Review:

Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.

The Sentient Machine

The Sentient Machine
  • Author : Amir Husain
  • Publisher : Simon and Schuster
  • Pages : 224
  • Relase : 2017-11-21
  • ISBN : 9781501144677

The Sentient Machine Book Review:

Explores universal questions about humanity's capacity for living and thriving in the coming age of sentient machines and AI, examining debates from opposing perspectives while discussing emerging intellectual diversity and its potential role in enabling a positive life.

The Book of Why

The Book of Why
  • Author : Judea Pearl,Dana Mackenzie
  • Publisher : Basic Books
  • Pages : 432
  • Relase : 2018-05-15
  • ISBN : 9780465097616

The Book of Why Book Review:

A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

Grokking Algorithms

Grokking Algorithms
  • Author : Aditya Bhargava
  • Publisher : Simon and Schuster
  • Pages : 256
  • Relase : 2016-05-12
  • ISBN : 9781638353348

Grokking Algorithms Book Review:

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. Continue your journey into the world of algorithms with Algorithms in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/algorithms-​in-motion). 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 step-by-step 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 trade-offs between algorithms Python-based code samples About the Reader This easy-to-read, picture-heavy introduction is suitable for self-taught 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 Breadth-first search Dijkstra's algorithm Greedy algorithms Dynamic programming K-nearest neighbors

Mathematics for Machine Learning

Mathematics for Machine Learning
  • Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
  • Publisher : Cambridge University Press
  • Pages : 398
  • Relase : 2020-03-31
  • ISBN : 9781108470049

Mathematics for Machine Learning Book Review:

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Interpretable Machine Learning

Interpretable Machine Learning
  • Author : Christoph Molnar
  • Publisher : Lulu.com
  • Pages : 314
  • Relase : 2019
  • ISBN : 9780244768522

Interpretable Machine Learning Book Review:

The Digital Mind

The Digital Mind
  • Author : Arlindo Oliveira
  • Publisher : MIT Press
  • Pages : 317
  • Relase : 2017-03-17
  • ISBN : 9780262036030

The Digital Mind Book Review:

The Red Queen's race -- The exponential nature of technology -- From Maxwell to the Internet -- The universal machine -- The quest for intelligent machines -- Cells, bodies, and brains -- Biology meets computation -- How the brain works -- Understanding the brain -- Brains, minds, and machines -- Challenges and promises -- Speculations

Superintelligence

Superintelligence
  • Author : Nick Bostrom
  • Publisher : Oxford University Press (UK)
  • Pages : 328
  • Relase : 2014
  • ISBN : 9780199678112

Superintelligence Book Review:

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. Other animals have stronger muscles or sharper claws, but we have cleverer brains. If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to 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 AI or otherwise to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? To get closer to an answer to this question, we must make our way through a fascinating landscape of topics and considerations. Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence.

The YouTube Formula

The YouTube Formula
  • Author : Derral Eves
  • Publisher : John Wiley & Sons
  • Pages : 352
  • Relase : 2021-02-24
  • ISBN : 9781119716020

The YouTube Formula Book Review:

The Wall Street Journal bestseller! Learn the secrets to getting dramatic results on YouTube Derral Eves has generated over 60 billion views on YouTube and helped 24 channels grow to one million subscribers from zero. In The YouTube Formula: How Anyone Can Unlock the Algorithm to Drive Views, Build an Audience, and Grow Revenue, the owner of the largest YouTube how-to channel provides the secrets to getting the results that every YouTube creator and strategist wants. Eves will reveal what readers can't get anywhere else: the inner workings of the YouTube algorithm that's responsible for determining success on the platform, and how creators can use it to their advantage. Full of actionable advice and concrete strategies, this book teaches readers how to: Launch a channel Create life-changing content Drive rapid view and subscriber growth Build a brand and increase engagement Improve searchability Monetize content and audience Replete with case studies and information from successful YouTube creators, The YouTube Formula is perfect for any creator, entrepreneur, social media strategist, and brand manager who hopes to see real commercial results from their work on the platform.

Understanding Machine Learning

Understanding Machine Learning
  • Author : Shai Shalev-Shwartz,Shai Ben-David
  • Publisher : Cambridge University Press
  • Pages : 409
  • Relase : 2014-05-19
  • ISBN : 9781107057135

Understanding Machine Learning Book Review:

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

What Algorithms Want

What Algorithms Want
  • Author : Ed Finn
  • Publisher : MIT Press
  • Pages : 257
  • Relase : 2017-03-10
  • ISBN : 9780262035927

What Algorithms Want Book Review:

The gap between theoretical ideas and messy reality, as seen in Neal Stephenson, Adam Smith, and Star Trek. We depend on—we believe in—algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman's curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking. Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things. If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of “algorithmic reading” and scholarship that attends to process, spearheading a new experimental humanities.

Machine Learning in Action

Machine Learning in Action
  • Author : Peter Harrington
  • Publisher : Simon and Schuster
  • Pages : 384
  • Relase : 2012-04-03
  • ISBN : 9781638352457

Machine Learning in Action Book Review:

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce