100 Best Machine Learning Books of All Time
We've researched and ranked the best machine learning books in the world, based on recommendations from world experts, sales data, and millions of reader ratings. Learn more
By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn 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...
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Recommended by Mark Tabladillo, and 1 others. Mark TabladilloBook to Start You on Machine Learning - KDnuggets https://t.co/19fdX59b0d This book is “Hands-On Machine Learning with Scikit-Learn & TensorFlow”. each new revision has become an even better version of one of the best in-depth resources to learn Machine Learning by doing. https://t.co/ujyUH3xU3e (Source)
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... more
Nassim Nicholas TalebVery clear exposition, does the math without getting lost in the details. Although many of the concepts of the introductory first 100 pages can be found elsewhere, they are presented with remarkable cut-to-the-chase clarity. (Source)
Satya NadellaElon Musk and Facebook AI chief Yann LeCun have praised this textbook on one of software’s most promising frontiers. After its publication, Microsoft signed up coauthor Bengio, a pioneer in machine learning, as an adviser (Source)
Recommended by Roger D. Peng, and 1 others. Roger D. PengThis book is written by a powerhouse of authors in the machine learning community, true authorities in the field. But beyond that, they’re also great writers. (Source)
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering... more
Recommended by Kirk Borne, and 1 others. Kirk Borne[Book] #MachineLearning — a Probabilistic Perspective: https://t.co/wAZwLoUFGF ———— #BigData #Statistics #DataScience #DeepLearning #AI #Algorithms #StatisticalLiteracy #Mathematics #abdsc ——— ⬇Get this brilliant 1100-page 28-chapter highly-rated book: https://t.co/Tm2zchpHSu https://t.co/jprUDdzkj8 (Source)
Concise and to the point — the book can be read during a week. During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.
Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.
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Recommended by Kirk Borne, and 1 others. Kirk BorneRecent top-selling books in #AI & #MachineLearning: https://t.co/Ij9I7SzR4d ————— #BigData #DataScience #DataMining #Algorithms #PredictiveAnalytics #Python ————— ...in the TOP 10: 1)The Hundred-Page ML Book: https://t.co/dQ7nP6gwP0 2)Hands-on ML with...: https://t.co/Y0Iz3GbtGP https://t.co/72rAFN1FwW (Source)
In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of... more
Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search... more
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... more
Maria RamosRamos will take the summer to examine some of the questions weighing more heavily on humankind as we contemplate our collective future: what happens when we can write our own genetic codes, and what happens when we create technology that is meaningfully more intelligent than us. The Gene: An Intimate History—Siddhartha Mukherjee Superintelligence: Paths, Dangers, Strategies—Nick Bostrom The... (Source)
Will MacAskillI picked this book because the possibility of us developing human-level artificial intelligence, and from there superintelligence—an artificial agent that is considerably more intelligent than we are—is at least a contender for the most important issue in the next two centuries. Bostrom’s book has been very influential in effective altruism, lots of people work on artificial intelligence in order... (Source)
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the... more
Recommended by Francesco Marconi, and 1 others. Francesco MarconiTop programming languages ranked by its annual search engine popularity. Python has gained momentum because of its importance to machine learning development. At @WSJ we are using it to build tools for journalists. Tip: this is a great book for anyone who wants to get started! https://t.co/ZsHjqB5gvC (Source)
Recommended by Daniel H Wilson, and 1 others. Daniel H WilsonYes, Machine Learning is a textbook and I would call it the textbook for machine learning and artificial intelligence. Machine learning is just the math of teaching a machine how to solve a problem on its own, because you’re not going to be able to be there to solve it for the machine. It can be any kind of problem: it could be a robot that needs to figure out how to get from point A to point B... (Source)
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... more
Recommended by Zachary Lipton, and 1 others. Zachary Lipton@innerproduct 1. Tor Lattimore Great book work on bandits (https://t.co/gttspSm40W) and work on causality + bandits (https://t.co/lkwvtEiKvE) 2. Caroline Uhler — Interesting work on causal inference + discovery, causal inference under measurement error etc (https://t.co/I3IRpwmdMd) (Source)
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the...
moreRecommended by Nassim Nicholas Taleb, and 1 others. Nassim Nicholas TalebVery comprehensive, sufficiently technical to get most of the plumbing behind machine learning. Very useful as a reference book (actually, there is no other complete reference book). The authors are the real thing (Tibshirani is the one behind the LASSO regularization technique). Uses some mathematical statistics without the burdens of measure theory and avoids the obvious but complicated... (Source)
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... more
Recommended by Vinod Khosla, and 1 others. Vinod KhoslaIf you want speculation about what the master AI might need (one view). For a slightly more technical read, I’d suggest Ian Goodfellows Deep Learning. (Source)
The Singularity Is Near portrays what life will be like after this event--a human-machine civilization where our experiences shift from real reality to virtual reality and where our intelligence becomes nonbiological and... more
Mark O'ConnellI wouldn’t be the first to look at him this way but I read Kurzweil’s work as essentially a work of religious mysticism. I think there’s no other way to read it, really. (Source)
Antonio EramThis book was recommended by Antonio when asked for titles he would recommend to young people interested in his career path. (Source)
Steve AokiIt opened me up to the idea of science fiction becoming science fact. (Source)
Yuval Noah HarariA superb and very timely survey of the impact of AI on the geopolitical system, the job market and human society. (Source)
Arianna HuffingtonKai-Fu Lee's experience as an AI pioneer, top investor, and cancer survivor has led to this brilliant book about global technology. AI Superpowers gives us a guide to a future that celebrates all the benefits that AI will bring, while cultivating what is unique about our humanity. It’s one of those books you read and think, ‘Why are people reading any other book right now when this is so clearly... (Source)
Satya NadellaKai-Fu Lee's smart analysis on human-AI coexistence is clear-eyed and a must-read. We must look deep within ourselves for the values and wisdom to guide AI's development. (Source)
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data... more
Thorsten HellerThe Best #book to Start your #DataScience Journey - Towards #DataScience https://t.co/D8PlkkSxw6 by @benthecoder1 (Source)
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... more
Barack ObamaAs 2018 draws to a close, I’m continuing a favorite tradition of mine and sharing my year-end lists. It gives me a moment to pause and reflect on the year through the books I found most thought-provoking, inspiring, or just plain loved. It also gives me a chance to highlight talented authors – some who are household names and others who you may not have heard of before. Here’s my best of 2018... (Source)
Bill GatesAnyone who wants to discuss how artificial intelligence is shaping the world should read this book. (Source)
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll... more
Recommended by Kirk Borne, and 1 others. Kirk BorneGreat book for Business Analytics and for building #AnalyticThinking >> “#DataScience for Business — What You Need to Know about #DataMining and Data-Analytic Thinking”: https://t.co/e9rAFnVYYQ #BigData #MachineLearning #DataStrategy #AnalyticsStrategy #Algorithms https://t.co/yEblfU2MZd (Source)
Recommended by Kirk Borne, and 1 others. Kirk BorneFind more than 40 useful #PredictiveModeling articles here at @DataScienceCtrl https://t.co/KdcvLRffRk #abdsc ———— #BigData #DataScience #AI #MachineLearning #Forecasting #Statistics #PredictiveAnalytics ——— +This is the best book on the subject: https://t.co/SmsepmniHi https://t.co/amBJHCJSHN (Source)
Programming Collective Intelligence takes you into the world of machine learning... more
Steve Jurvetson[Steve Jurvetson recommended this book on the podcast "The Tim Ferriss Show".] (Source)
Seth GodinIn the last week, I discovered that at least two of my smart friends hadn't read Godel, Escher, Bach. They have now. You should too. (Source)
Kevin KellyOver the years, I kept finding myself returning to its insights, and each time I would arrive at them at a deeper level. (Source)
Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing... more
Recommended by Kirk Borne, and 2 others. Kirk Borne✨🎉🌟Must see this >> Free #Python #DataScience Coding book series for #DataScientists ...via @DataScienceCtrl Go to https://t.co/To10VVZzIl ——————— #abdsc #BigData #MachineLearning #AI #DeepLearning #BeDataBrilliant #DataLiteracy https://t.co/Msuo1jiZSm (Source)
Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data,... more
"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,... more
D.a. Wallach@EricTopol @yudapearl @bschoelkopf @MPI_IS I love @yudapearl 's book so much! Profound, heterodox. (Source)
Kirk Borne.@yudapearl wrote the awesome "Book of Why", but he recommends this fun and less #mathematics-heavy read >> his #AI lecture given in 1999: https://t.co/kNYIoJ8qcY #DataScience #MachineLearning #Statistics #BookofWhy #Causalinference #Bayes https://t.co/CNQlKP8cU3 (Source)
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
* Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
* Deep learning, a powerful set of techniques for learning in neural networks
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts... more
In the popular imagination, superhuman artificial intelligence is an approaching tidal wave that threatens not just jobs and human relationships, but civilization itself. Conflict between humans and machines is seen as inevitable and its outcome all too predictable.
In this groundbreaking book, distinguished AI researcher Stuart Russell argues that this scenario can be avoided, but only if we... more
Diane Coylethere’s a whole clutch of AI books…People want to understand what’s going on. Human Compatible is a really clearly written one. It explains enough about how AI works, but also what some of the challenges are. (Source)
Marcus BorbaBook Review, ‘Human Compatible’: A Book About Artificial Intelligence (#AI) That Asks Some Interesting Questions https://t.co/BCe5JnHPuE @Forbes #ArtificialIntelligence #DataScience #BigData #DeepLearning #Robotics #MachineLearning https://t.co/gKo0mpBeva (Source)
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers... more
Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines.
Kurzweil discusses how the brain functions, how the mind emerges from the brain, and the implications... more
Naveen JainThen the great book that Ray Kurzweil wrote, "How to Create a Mind" really tells you about how human brain works. (Source)
New York Times Bestseller
A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric
We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is... more
Paula BoddingtonHow the use of algorithms has affected people’s lives and occasionally ruined them. (Source)
Ramesh SrinivasanThis book is a really fantastic analysis of how quantification, the collection of data, the modelling around data, the predictions made by using data, the algorithmic and quantifiable ways of predicting behaviour based on data, are all built by elites for elites and end up, quite frankly, screwing over everybody else. (Source)
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
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Recommended by Eric Weinstein, and 1 others. Eric Weinstein[Eric Weinstein recommended this book on Twitter.] (Source)
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
Written by Wes McKinney, the main author of the pandas library, Python for Data Analysis also serves as a practical, modern introduction to scientific computing in Python for data-intensive... more
New York Times Bestseller
"Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War…could turn out to be one of the more momentous books of the decade."
-New York Times Book Review
"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century."
-Rachel Maddow, author of Drift
"A serious... more
Recommended by Bill Gates, and 1 others. Bill GatesAnyone interested in politics may be attracted to Nate Silver’s The Signal and the Noise: Why So Many Predictions Fail—but Some Don't. Silver is the New York Times columnist who got a lot of attention last fall for predicting—accurately, as it turned out–the results of the U.S. presidential election. This book actually came out before the election, though, and it’s about predictions in many... (Source)
In The Second Machine Age MIT's Erik Brynjolfsson and Andrew McAfee—two thinkers at the forefront of their field—reveal the forces driving the...
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Michael DellThe authors make a case for a future world that is better, not worse, than the one we inherited. That may seem far-fetched given the problems we see flashing across our screens every day. But there is reason for optimism, and it starts and ends with one of my favorite things, technology. (Source)
Dominic Steil[One of the books that had the biggest impact on .] (Source)
Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.
Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can... more
Joan BoixadosI’m reading “On intelligence” by Jeff Hawkins. I am really enjoying it. It’s a very specific theory of how our brain learns and makes predictions (the root of our intelligence) explained for average people unfamiliar with the field. It’s also very related to computer science and artificial intelligence since it tried to prove the current approaches to those are flawed. I’m getting a better... (Source)
But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Data science is little more than using straight-forward steps to process raw data into... more
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
Stronger focus on MCMC Revision of the computational advice in Part... more
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... more
Lawrence SummersAI may transform your life. And Prediction Machines will transform your understanding of AI. This is the best book yet on what may be the best technology that has come along. (Source)
Dominic BartonPrediction Machines achieves a feat as welcome as it is unique: a crisp, readable survey of where artificial intelligence is taking us separates hype from reality, while delivering a steady stream of fresh insights. It speaks in a language that top executives and policy makers will understand. Every leader needs to read this book. (Source)
Kevin KellyThis book makes artificial intelligence easier to understand by recasting it as a new, cheap commodity--predictions. It's a brilliant move. I found the book incredibly useful. (Source)
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... more
Recommended by Ana Bell, and 1 others. 
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in... more
Packed with examples and exercises, Natural... more
Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your... more
Kirk Borne✨🎉🌟Must see this >> Free #Python #DataScience Coding book series for #DataScientists ...via @DataScienceCtrl ——————————————— #abdsc #Coding #MachineLearning #BigData #BeDataBrilliant #DataLiteracy 👇👇👇 https://t.co/l14zcnYlb7 (Source)
Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Technology
Machine learning systems help you find valuable insights and patterns in data,... more
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.
All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of... more
Nassim Nicholas Taleb@Stefano_Peron This is the BEST book (Source)
Eric WeinsteinFolks frequently ask “What are the books that changed your life?” If I tell them, they are usually radically disappointed. I find that curious. I just cleared out of an office, and these are 4 shelves of spines of books that mattered enough to me to bring home. So here they are. (Source)
If you were accused of a crime, who would you rather decide your sentence—a mathematically consistent algorithm incapable of empathy or a compassionate human judge prone to bias and error? What if you want to buy a driverless car and must choose between one programmed to save as many lives as possible and another that prioritizes the lives of its own passengers? And would you agree to share your family’s full medical history if you were told that it would help researchers find a cure for... more
David SmithDarroch: “The best book I’ve read recently is called Hello World... It’s about the impact of algorithms across different areas... For me this was the best piece of learning I’ve done in recent months.” (Source)
Jim Al-KhaliliThe fact is, the age of AI is coming fast, and we need to be ready for it. This book will help you decide how worried you should be. (Source)
The ability to take raw data, access it, filter it, process it, visualize it, understand it, and communicate it to others is possibly the most essential business problem for the coming decades. "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data.
Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data...
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning... more
Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal... more
Recommended by Roger D. Peng, and 1 others. Roger D. PengIt’s important to think in terms of what your audience needs, and what would be best for them among the many choices you could make when analysing data. (Source)
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
--Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning
Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline's techniques. Packed with... more
Recommended by Kirk Borne, and 1 others. Kirk Borne🌟📘📊📈Awesome new book >> #DeepLearning Illustrated — A Visual, Interactive Guide to Artificial Intelligence” https://t.co/xIW48MskrR by @JonKrohnLearns ——————— #BigData #Analytics #DataScience #AI #MachineLearning #Algorithms #NeuralNetworks https://t.co/JKSrVRLpS0 (Source)
Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our world—provided we ask the right questions.
By the end of an average day in the early twenty-first century, human beings searching the internet will amass eight trillion gigabytes of data. This staggering amount of information—unprecedented in history—can tell us a great deal about who we... more
Jj. Omojuwa@SympLySimi Lol. Read this book. You’d love it. https://t.co/d2cLOyoiZ9 (Source)
Ron FournierJust finished, “Everybody Lies” by @SethS_D, which in addition to being a tremendous education on Big Data, includes the best conclusion to a non-fiction book I’ve ever read. Read it. -30- (Source)
All our lives are constrained by limited space and time, limits that give rise to a particular set of problems. What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of new activities and familiar favorites is the most fulfilling? These may seem like uniquely human quandaries, but they are not: computers, too, face the same... more
Doug McMillonHere are some of my favorite reads from 2017. Lots of friends and colleagues send me book suggestions and it's impossible to squeeze them all in. I continue to be super curious about how digital and tech are enabling people to transform our lives but I try to read a good mix of books that apply to a variety of areas and stretch my thinking more broadly. (Source)
Sriram Krishnan@rabois @nealkhosla Yes! Love that book (Source)
Chris OliverThis is a great book talking about how you can use computer science to help you make decisions in life. How do you know when to make a decision on the perfect house? Car? etc? It helps you apply algorithms to making those decisions optimally without getting lost. (Source)
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn... more
Recommended by Elon Musk, and 1 others. 
Authors Adam Gibson and Josh Patterson present the latest relevant papers and techniques in a nonacademic manner, and implement the core mathematics in their DL4J library. If you work in the embedded, desktop, and big data/Hadoop spaces and really want to understand deep learning, this is your book. less
The text presents generalized linear multilevel models from a Bayesian perspective,... more
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.
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,... more
Recommended by Robert Went, and 1 others. Robert WentReading ‘You look like a thing and I love you: How AI works and why it is making the world a weirder place’, a wonderful book by @JanelleCShane — very funny, and I learn a lot https://t.co/SaZPjRTdVw (Source)
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... more
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... more
In "Automate the Boring Stuff with Python," you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand no prior programming experience required. Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to: Search for text in a file or across multiple... more
Oxford Landmark Science books are 'must-read' classics of modern science writing which have crystallized big ideas, and shaped the way we think. less
Recommended by Jim Al-Khalili, and 1 others. Jim Al-KhaliliIf things are left to their own devices they decay and unwind and disorder increases. If you take a pack of cards in the right order and shuffle it they will get mixed up. (Source)
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
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This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis,... more
Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science's AI program, demonstrates key ML concepts with code snippets,... more
Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into... more
With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You'll also learn how to... more
On Aurora, the first and greatest of the Spacer planets, Elijah Baley and R. Daneel Olivaw investigate yet another seemingly impossible crime - this time, a roboticide.
Someone has destroyed the positronic mind of R. Jander Panell, a humanoid twin to Daneel. His... more
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 AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.
In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How... more
Recommended by David Shaywitz, and 1 others. David ShaywitzReally enjoying, and have nearly completed (via @audible_com) @MelMitchell1 fascinating new book on AI. https://t.co/feb6t8qwMJ highly recommended! And h/t to @kevinhorgan for yet another splendid suggestion. (Source)
Don't have time to read the top Machine Learning books of all time? Read Shortform summaries.
Shortform summaries help you learn 10x faster by:
- Being comprehensive: you learn the most important points in the book
- Cutting out the fluff: you focus your time on what's important to know
- Interactive exercises: apply the book's ideas to your own life with our educators' guidance.