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About the Author
Max Pumperla is a Data Scientist and Engineer specializing in Deep Learning at the artificial intelligence company skymind.ai. He is the co-founder of the Deep Learning platform aetros.com.Kevin Ferguson has 18 years of experience in distributed systems and data science. He is a data scientist at Honor, and has experience at companies such as Google and Meebo.
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Product details
Paperback: 384 pages
Publisher: Manning Publications; 1 edition (January 25, 2019)
Language: English
ISBN-10: 1617295329
ISBN-13: 978-1617295324
Product Dimensions:
7.2 x 0.8 x 9 inches
Shipping Weight: 1.4 pounds (View shipping rates and policies)
Average Customer Review:
5.0 out of 5 stars
2 customer reviews
Amazon Best Sellers Rank:
#36,878 in Books (See Top 100 in Books)
Although the title is "Deep Learning and the Game of Go", the focus (and strength) of this book is Deep Reinforcement learning (DRL), one of the hottest Machine Learning topics right now. The authors present the major new DRL algorithms by applying them to the ancient game of Go, which - and this is what makes this an enjoyable read - neither so easy that you constantly ask yourself "what do I need these fancy techniques for?" nor so hard as to require Google-scale hardware to get to end up with a bit that you can perceive to play intelligently (though not at the AlphaGo level). One of the strengths of the book compared to others on DRL is that it does not shy away from the question of how to obtain training data, preprocess it and encode the game states, which makes it feel quite self-contained and useful on its own.To get the most out of this book you should be a proficient software developer (not necessarily in Python), both to make sense of the numerous code snippets and to get the examples to run (great care has been taken to present self-contained and runnable code in the first few chapters, but expect to run into some minor hiccups with the code in later sections), but also to have a go at adapting the ideas to another game (or games?) of your choice.You should also have at least some interest in Go, but it's not necessary to be able to actually play Go (I myself was familiar with the rules, but apart from that am a total Go beginner).The book starts with a discussion of go rules, how to implement them and how to efficiently represent a go board and moves in code. There's a brief interlude on the basics of neural networks, followed by a first implementation of a Go bot using two main ideas that will be central to the later chapters: tree search methods and training a neural network to predict moves. After the already mentioned extremely useful chapter on how to learn from real data and how to get actual data in the first place, there's a rather optional chapter on using the cloud to train your bot and deploying it onto a public Go server. The next few chapters on the different reinforcement learning methods give sufficient detail to understand the code and the main ideas behind these algorithms without becoming overly theoretical. The final part is dedicated to an in-depth look at how AlphaGo and AlphaGo Zero achieved their stellar performance.
a long time ago, i used to write code for Go boards to support 2 or more simultaneous players. i never imagined the day would come where making an AI opponent would be feasible. this is an extremely good way to learn the machine learning topic; to focus on an actual application to the end.most books focus on classifying images, and are pretty good for that. if you are trying to make an AI to actually control a system, then this book should be a lot more relevant.
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