move code, add toc and cover to readme

README.md

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 # Deep Learning and the Game of Go
 
-Code and other material for the book "Deep Learning and the Game of Go"
+[Code](https://github.com/maxpumperla/deep_learning_and_the_game_of_go/code), sample chapters and notebooks for the book "Deep Learning and the Game of Go" (Manning), available for early access [here](https://www.manning.com/books/deep-learning-and-the-game-of-go).
+
+![dl_go_cover](dl_go_cover.jpg)
+
+# Table of Contents
+
+1. Towards deep learning: a machine learning introduction [code]() | [code]()
+2. Machine learning for go
+3. Representing a game of go: a simple search bot
+4. Search and the early years of game AI
+5. Getting started with neural networks
+6. Designing a neural network for Go data
+7. Learning from data: a deep learning bot
+8. Enter deep reinforcement learning
+9. Reinforcement learning with the policy gradient algorithm
+10. Reinforcement learning with value methods
+11. Reinforcement with actor-critic methods
+12. AlphaGo: Combining approaches
+13. Bots in the wild: deployment and scale-out
+
+![chapter_succession](chapter_succession.png)
+
+# Welcome
+
+When AlphaGo hit the news in early 2016, we were extremely excited about this groundbreaking advancement in computer Go. At the time, it was largely conjectured that human-level artificial intelligence for the game of Go was at least 10 years in the future. We followed the games meticulously and didn’t shy away from waking up early or staying up late to watch the broadcasted games live. Indeed, we had good company — millions of people around the globe were captivated by the games against Fan Hui, Lee Sedol, and later, Ke Jie and others.
+
+Shortly after the emergence of AlphaGo, we picked up work on a little open source library we coined [BetaGo](https://github.com/maxpumperla/betago) to see if we could implement some of the core mechanisms running AlphaGo ourselves. The idea of BetaGo was to bring AlphaGo to regular developers. While we were realistic enough to accept that we didn’t have the resources (time, computing power, or intelligence) to compete with Deepmind's incredible achievement, it has been a lot of fun to create our own Go bot. Since then, we’ve had the privilege to speak about computer Go at quite a few occasions.
+
+We strongly believe that the principles underpinning AlphaGo can be taught to a general software engineering audience in a practical manner. Enjoyment and understanding of Go comes from playing it and experimenting with it. It can be argued that the same holds true for machine learning, or any other discipline for that matter. In this book, we hope to use the game of Go as a gateway to the exciting world of deep learning. We start with the classical AI principles for board games. Right from the start, you’ll have a working Go AI that you can play against—although it will be very weak at first. Then we’ll introduce some techniques from the world of deep learning and reinforcement learning. As you learn each technique, you can incorporate it into your Go AI and watch it improve.
+
+If you share some of our enthusiasm for either Go or machine learning, hopefully both, at the end of this book, we’ve done our job. If, on top of that, you know how to build and ship a Go bot and run your own experiments, many other interesting artificial intelligence applications will be accessible to you as well. Enjoy the ride!