- Copyright
- Brief Table of Contents
- Table of Contents
- Foreword
- Preface
- Acknowledgments
- About this book
- About the authors
- About the cover illustration
- Part 1. Foundations
- Part 2. Machine learning and game AI
- Chapter 4. Playing games with tree search
- Chapter 5. Getting started with neural networks
- Chapter 6. Designing a neural network for Go data
- 6.1. Encoding a Go game position for neural networks
- 6.2. Generating tree-search games as network training data
- 6.3. Using the Keras deep-learning library
- 6.4. Analyzing space with convolutional networks
- 6.5. Predicting Go move probabilities
- 6.6. Building deeper networks with dropout and rectified linear units
- 6.7. Putting it all together for a stronger Go move-prediction network
- 6.8. Summary
- Chapter 7. Learning from data: a deep-learning bot
- 7.1. Importing Go game records
- 7.2. Preparing Go data for deep learning
- 7.3. Training a deep-learning model on human game-play data
- 7.4. Building more-realistic Go data encoders
- 7.5. Training efficiently with adaptive gradients
- 7.6. Running your own experiments and evaluating performance
- 7.7. Summary
- Chapter 8. Deploying bots in the wild
- 8.1. Creating a move-prediction agent from a deep neural network
- 8.2. Serving your Go bot to a web frontend
- 8.3. Training and deploying a Go bot in the cloud
- 8.4. Talking to other bots: the Go Text Protocol
- 8.5. Competing against other bots locally
- 8.6. Deploying a Go bot to an online Go server
- 8.7. Summary
- Chapter 9. Learning by practice: reinforcement learning
- Chapter 10. Reinforcement learning with policy gradients
- Chapter 11. Reinforcement learning with value methods
- Chapter 12. Reinforcement learning with actor-critic methods
- Part 3. Greater than the sum of its parts
- Chapter 13. AlphaGo: Bringing it all together
- Chapter 14. AlphaGo Zero: Integrating tree search with reinforcement learning
- 14.1. Building a neural network for tree search
- 14.2. Guiding tree search with a neural network
- 14.3. Training
- 14.4. Improving exploration with Dirichlet noise
- 14.5. Modern techniques for deeper neural networks
- 14.6. Exploring additional resources
- 14.7. Wrapping up
- 14.8. Summary
- Vectors, matrices, and beyond: a linear algebra primer
- Rank 3 tensors
- Calculus in five minutes: derivatives and finding maxima
- A bit of notation
- The backpropagation algorithm for feed-forward networks
- Backpropagation for sequential neural networks
- Backpropagation for neural networks in general
- Computational challenges with backpropagation
- Go programs
- Go servers
- Model training on AWS
- Hosting a bot on AWS over HTTP
- Registering and activating your bot at OGS
- Testing your OGS bot locally
- Deploying your OGS bot on AWS
- Appendix A. Mathematical foundations
- Appendix B. The backpropagation algorithm
- Appendix C. Go programs and servers
- Appendix D. Training and deploying bots by using Amazon Web Services
- Appendix E. Submitting a bot to the Online Go Server
- Index
- List of Figures
- List of Tables
- List of Listings
Deep Learning and the Game of Go
Max Pumperla and Kevin Ferguson
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