About the book: A widely used text on reinforcement learning, which is one of the most active research areas in artificial intelligence, this book provides a clear and simple account of the field's key ideas and algorithms. With a focus on core online learning algorithms, including UCB, Expected Sarsa, and Double Learning, it then extends these ideas to function approximation covering topics on artificial neural networks and the Fourier basis. This second edition includes new chapters on reinforcement learning's relationships to psychology and neuroscience as well as updated case-studies on AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. About the authors: Richard S. Sutton is a distinguished research scientist at DeepMind in Edmonton and a professor in the Department of Computing Science at the University of Alberta. He previously worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts.

You should look at your data. Graphs and charts let you explore and learn about the structure of the information you collect. Good data visualizations also make it easier to communicate your ideas and findings to other people. Beyond that, producing effective plots from your own data is the best way to develop a good eye for reading and understanding graphs--good and bad--made by others, whether presented in research articles, business slide decks, public policy advocacy, or media reports. This book teaches you how to do it.

Why you need to improve your training data, and how to do it https://t.co/swEv2nzBoE

The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining.

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This title covers Neural networks in depth. Neural networks are a bio-inspired mechanism of data processing, that enables computers to learn technically similar to a brain and even generalize once solutions to enough problem instances are taught.

We continued to cover interesting and important stories in the intersecting fields of AI, Analytics, Big Data, Data Science, Deep Learning, and Machine Learning. We published over 1200 stories, had over 4.8 million visitors who saw over 14 million pages. Our subscribers have grown to over 200,000 subscribers/followers via email and social media. Here are the top KDnuggets stories from 2017 - read them, if you have not already. How to understand Gradient Descent algorithm, by Jahnavi Mahanta 17 More Must-Know Data Science Interview Questions and Answers, by Gregory Piatetsky 6 Reasons Why Python Is Suddenly Super Popular, by Kayla Matthews 7 More Steps to Mastering Machine Learning With Python, by Matthew Mayo Here are the most shared stories in 2017.

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This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

For the month of April, we also recognize the most popular posts and blogger based on unique page views (UPV) and social shares. Most Viewed and Most Shared - Platinum Badge ( 20,000 UPV AND 2,000 shares) 10 Free Must-Read Books for Machine Learning and Data Science, by Matthew Mayo Most Viewed - Gold Badges ( 10,000 UPV) Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions, by Gregory Piatetsky Top 20 Recent Research Papers on Machine Learning and Deep Learning, by Thuy Pham Most Viewed - Silver Badges ( 5,000 unique PV) Awesome Deep Learning: Most Cited Deep Learning Papers, by Terry Taewoong Um 5 Machine Learning Projects You Can No Longer Overlook, April, by Matthew Mayo Keep it simple! How to understand Gradient Descent algorithm, by Jahnavi Mahanta Top mistakes data scientists make when dealing with business people, by Karolis Urbonas (new) New Online Data Science Tracks for 2017, by Brendan Martin (new) Cartoon: Machine Learning - What They Think I Do, by Harrison Kinsley Data Science for the Layman (No Math Added), Annalyn Ng and Kenneth Soo Most Shared - Gold Badges ( 1,000 shares) Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions, by Gregory Piatetsky Top 20 Recent Research Papers on Machine Learning and Deep Learning, by Thuy Pham Top mistakes data scientists make when dealing with business people, by Karolis Urbonas (new) Awesome Deep Learning: Most Cited Deep Learning Papers, by Terry Taewoong Um Most Shared - Silver Gold Badges ( 500 shares) Keep it simple! How to understand Gradient Descent algorithm A Brief History of Artificial Intelligence, By Francesco Corea The 42 V's of Big Data and Data Science, by Tom Shafer (new) Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Intelligence, by Matthew Mayo (new) Awesome Deep Learning: Most Cited Deep Learning Papers, by Terry Taewoong Um 5 Machine Learning Projects You Can No Longer Overlook, April, by Matthew Mayo Keep it simple! How to understand Gradient Descent algorithm A Brief History of Artificial Intelligence, By Francesco Corea The 42 V's of Big Data and Data Science, by Tom Shafer (new) Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Intelligence, by Matthew Mayo (new)