Deep learning and the game of Go / Max Pumperla and Kevin Ferguson.

Includes index.

Part 1. Foundations.--1 Toward deep learning: a machine-learning introduction.--2. Go as a machine-learning problem.--3. Implementing your first Go bot.--Part 2. Machine learning and game AI.--4. Playing games with tree search.--5. Getting started with neural networks.--6. Designing a neural network for Go data.--7. Learning from data: a deep-learning bot.-- 8. Deploying bots in the wild.--9. Learning by practice: reinforcement learning.--10. Reinforcement learning with policy gradients.--Reinforcement learning with value methods.--12. Reinforcement learning with actor-critic methods--Part 3. Greater than the sum of its parts.--13 AlphaGo: Bringing it all together.--14 AlphaGo Zero: Integrating tree search with reinforcement learning.

The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! "Deep learning and the game of Go" introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios!-- Source other than Library of Congress.