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Deep Learning and the Game of Go Video Edition

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Files:

Get Bonus Downloads Here.url 180 bytes
001. Part 1. Foundations.en.srt 600 bytes
001. Part 1. Foundations.mp4 1.0 MB
002. Chapter 1. Toward deep learning - a machine-learning introduction.en.srt 18.0 KB
002. Chapter 1. Toward deep learning - a machine-learning introduction.mp4 29.5 MB
003. Chapter 1. Machine learning by example.en.srt 18.7 KB
003. Chapter 1. Machine learning by example.mp4 28.1 MB
004. Chapter 1. Deep learning.en.srt 7.1 KB
004. Chapter 1. Deep learning.mp4 11.0 MB
005. Chapter 1. What you ll learn in this book.en.srt 2.4 KB
005. Chapter 1. What you ll learn in this book.mp4 3.5 MB
006. Chapter 1. Summary.en.srt 2.3 KB
006. Chapter 1. Summary.mp4 5.9 MB
007. Chapter 2. Go as a machine-learning problem.en.srt 4.2 KB
007. Chapter 2. Go as a machine-learning problem.mp4 8.6 MB
008. Chapter 2. A lightning introduction to the game of Go.en.srt 12.7 KB
008. Chapter 2. A lightning introduction to the game of Go.mp4 23.0 MB
009. Chapter 2. Handicaps.en.srt 1.2 KB
009. Chapter 2. Handicaps.mp4 2.3 MB
010. Chapter 2. Where to learn more.en.srt 1.5 KB
010. Chapter 2. Where to learn more.mp4 2.9 MB
011. Chapter 2. What can we teach a machine.en.srt 9.7 KB
011. Chapter 2. What can we teach a machine.mp4 17.5 MB
012. Chapter 2. How to measure your Go AI s strength.en.srt 3.7 KB
012. Chapter 2. How to measure your Go AI s strength.mp4 6.9 MB
013. Chapter 2. Summary.en.srt 1.3 KB
013. Chapter 2. Summary.mp4 3.8 MB
014. Chapter 3. Implementing your first Go bot.en.srt 19.9 KB
014. Chapter 3. Implementing your first Go bot.mp4 29.0 MB
015. Chapter 3. Capturing game state and checking for illegal moves.en.srt 9.2 KB
015. Chapter 3. Capturing game state and checking for illegal moves.mp4 14.4 MB
016. Chapter 3. Ending a game.en.srt 6.4 KB
016. Chapter 3. Ending a game.mp4 10.6 MB
017. Chapter 3. Creating your first bot - the weakest Go AI imaginable.en.srt 4.9 KB
017. Chapter 3. Creating your first bot - the weakest Go AI imaginable.mp4 9.6 MB
018. Chapter 3. Speeding up game play with Zobrist hashing.en.srt 9.5 KB
018. Chapter 3. Speeding up game play with Zobrist hashing.mp4 20.0 MB
019. Chapter 3. Playing against your bot.en.srt 2.2 KB
019. Chapter 3. Playing against your bot.mp4 3.7 MB
020. Chapter 3. Summary.en.srt 1.6 KB
020. Chapter 3. Summary.mp4 3.9 MB
021. Part 2. Machine learning and game AI.en.srt 1.0 KB
021. Part 2. Machine learning and game AI.mp4 1.9 MB
022. Chapter 4. Playing games with tree search.en.srt 8.5 KB
022. Chapter 4. Playing games with tree search.mp4 17.1 MB
023. Chapter 4. Anticipating your opponent with minimax search.en.srt 6.9 KB
023. Chapter 4. Anticipating your opponent with minimax search.mp4 10.2 MB
024. Chapter 4. Solving tic-tac-toe - a minimax example.en.srt 4.8 KB
024. Chapter 4. Solving tic-tac-toe - a minimax example.mp4 9.5 MB
025. Chapter 4. Reducing search space with pruning.en.srt 21.0 KB
025. Chapter 4. Reducing search space with pruning.mp4 35.0 MB
026. Chapter 4. Evaluating game states with Monte Carlo tree search.en.srt 24.9 KB
026. Chapter 4. Evaluating game states with Monte Carlo tree search.mp4 44.2 MB
027. Chapter 4. Summary.en.srt 1.9 KB
027. Chapter 4. Summary.mp4 5.9 MB
028. Chapter 5. Getting started with neural networks.en.srt 25.2 KB
028. Chapter 5. Getting started with neural networks.mp4 45.7 MB
029. Chapter 5. The basics of neural networks.en.srt 4.6 KB
029. Chapter 5. The basics of neural networks.mp4 6.9 MB
030. Chapter 5. Feed-forward networks.en.srt 8.9 KB
030. Chapter 5. Feed-forward networks.mp4 20.3 MB
031. Chapter 5. How good are our predictions Loss functions and optimization.en.srt 21.0 KB
031. Chapter 5. How good are our predictions Loss functions and optimization.mp4 38.6 MB
032. Chapter 5. Training a neural network step-by-step in Python.en.srt 16.2 KB
032. Chapter 5. Training a neural network step-by-step in Python.mp4 30.0 MB
033. Chapter 5. Summary.en.srt 2.5 KB
033. Chapter 5. Summary.mp4 5.8 MB
034. Chapter 6. Designing a neural network for Go data.en.srt 10.4 KB
034. Chapter 6. Designing a neural network for Go data.mp4 22.1 MB
035. Chapter 6. Generating tree-search games as network training data.en.srt 5.5 KB
035. Chapter 6. Generating tree-search games as network training data.mp4 11.5 MB
036. Chapter 6. Using the Keras deep-learning library.en.srt 20.2 KB
036. Chapter 6. Using the Keras deep-learning library.mp4 34.4 MB
037. Chapter 6. Analyzing space with convolutional networks.en.srt 17.2 KB
037. Chapter 6. Analyzing space with convolutional networks.mp4 34.5 MB
038. Chapter 6. Predicting Go move probabilities.en.srt 11.1 KB
038. Chapter 6. Predicting Go move probabilities.mp4 23.5 MB
039. Chapter 6. Building deeper networks with dropout and rectified linear units.en.srt 5.9 KB
039. Chapter 6. Building deeper networks with dropout and rectified linear units.mp4 11.6 MB
040. Chapter 6. Putting it all together for a stronger Go move-prediction network.en.srt 6.2 KB
040. Chapter 6. Putting it all together for a stronger Go move-prediction network.mp4 12.5 MB
041. Chapter 6. Summary.en.srt 1.5 KB
041. Chapter 6. Summary.mp4 4.6 MB
042. Chapter 7. Learning from data - a deep-learning bot.en.srt 12.1 KB
042. Chapter 7. Learning from data - a deep-learning bot.mp4 23.6 MB
043. Chapter 7. Preparing Go data for deep learning.en.srt 22.9 KB
043. Chapter 7. Preparing Go data for deep learning.mp4 43.0 MB
044. Chapter 7. Training a deep-learning model on human game-play data.en.srt 12.1 KB
044. Chapter 7. Training a deep-learning model on human game-play data.mp4 25.9 MB
045. Chapter 7. Building more-realistic Go data encoders.en.srt 5.6 KB
045. Chapter 7. Building more-realistic Go data encoders.mp4 11.5 MB
046. Chapter 7. Training efficiently with adaptive gradients.en.srt 11.6 KB
046. Chapter 7. Training efficiently with adaptive gradients.mp4 20.6 MB
047. Chapter 7. Running your own experiments and evaluating performance.en.srt 14.3 KB
047. Chapter 7. Running your own experiments and evaluating performance.mp4 35.9 MB
048. Chapter 7. Summary.en.srt 1.5 KB
048. Chapter 7. Summary.mp4 4.3 MB
049. Chapter 8. Deploying bots in the wild.en.srt 10.2 KB
049. Chapter 8. Deploying bots in the wild.mp4 22.0 MB
050. Chapter 8. Serving your Go bot to a web frontend.en.srt 6.6 KB
050. Chapter 8. Serving your Go bot to a web frontend.mp4 12.0 MB
051. Chapter 8. Training and deploying a Go bot in the cloud.en.srt 2.7 KB
051. Chapter 8. Training and deploying a Go bot in the cloud.mp4 5.3 MB
052. Chapter 8. Talking to other bots - the Go Text Protocol.en.srt 6.6 KB
052. Chapter 8. Talking to other bots - the Go Text Protocol.mp4 14.2 MB
053. Chapter 8. Competing against other bots locally.en.srt 11.0 KB
053. Chapter 8. Competing against other bots locally.mp4 17.7 MB
054. Chapter 8. Deploying a Go bot to an online Go server.en.srt 6.2 KB
054. Chapter 8. Deploying a Go bot to an online Go server.mp4 12.0 MB
055. Chapter 8. Summary.en.srt 1.2 KB
055. Chapter 8. Summary.mp4 3.8 MB
056. Chapter 9. Learning by practice - reinforcement learning.en.srt 8.6 KB
056. Chapter 9. Learning by practice - reinforcement learning.mp4 15.5 MB
057. Chapter 9. What goes into experience.en.srt 8.7 KB
057. Chapter 9. What goes into experience.mp4 14.3 MB
058. Chapter 9. Building an agent that can learn.en.srt 15.1 KB
058. Chapter 9. Building an agent that can learn.mp4 26.8 MB
059. Chapter 9. Self-play - how a computer program practices.en.srt 9.7 KB
059. Chapter 9. Self-play - how a computer program practices.mp4 18.6 MB
060. Chapter 9. Summary.en.srt 1.9 KB
060. Chapter 9. Summary.mp4 5.5 MB
061. Chapter 10. Reinforcement learning with policy gradients.en.srt 11.0 KB
061. Chapter 10. Reinforcement learning with policy gradients.mp4 19.0 MB
062. Chapter 10. Modifying neural network policies with gradient descent.en.srt 12.2 KB
062. Chapter 10. Modifying neural network policies with gradient descent.mp4 21.8 MB
063. Chapter 10. Tips for training with self-play.en.srt 16.5 KB
063. Chapter 10. Tips for training with self-play.mp4 27.8 MB
064. Chapter 10. Summary.en.srt 1.8 KB
064. Chapter 10. Summary.mp4 5.1 MB
065. Chapter 11. Reinforcement learning with value methods.en.srt 12.4 KB
065. Chapter 11. Reinforcement learning with value methods.mp4 20.2 MB
066. Chapter 11. Q-learning with Keras.en.srt 13.8 KB
066. Chapter 11. Q-learning with Keras.mp4 24.1 MB
067. Chapter 11. Summary.en.srt 1.2 KB
067. Chapter 11. Summary.mp4 3.7 MB
068. Chapter 12. Reinforcement learning with actor-critic methods.en.srt 14.3 KB
068. Chapter 12. Reinforcement learning with actor-critic methods.mp4 25.2 MB
069. Chapter 12. Designing a neural network for actor-critic learning.en.srt 3.9 KB
069. Chapter 12. Designing a neural network for actor-critic learning.mp4 8.1 MB
070. Chapter 12. Playing games with an actor-critic agent.en.srt 1.0 KB
070. Chapter 12. Playing games with an actor-critic agent.mp4 2.1 MB
071. Chapter 12. Training an actor-critic agent from experience data.en.srt 11.2 KB
071. Chapter 12. Training an actor-critic agent from experience data.mp4 19.4 MB
072. Chapter 12. Summary.en.srt 2.0 KB
072. Chapter 12. Summary.mp4 3.6 MB
073. Part 3. Greater than the sum of its parts.en.srt 979 bytes
073. Part 3. Greater than the sum of its parts.mp4 2.1 MB
074. Chapter 13. AlphaGo - Bringing it all together.en.srt 26.5 KB
074. Chapter 13. AlphaGo - Bringing it all together.mp4 51.5 MB
075. Chapter 13. Bootstrapping self-play from policy networks.en.srt 3.9 KB
075. Chapter 13. Bootstrapping self-play from policy networks.mp4 8.1 MB
076. Chapter 13. Deriving a value network from self-play data.en.srt 1.8 KB
076. Chapter 13. Deriving a value network from self-play data.mp4 3.5 MB
077. Chapter 13. Better search with policy and value networks.en.srt 25.1 KB
077. Chapter 13. Better search with policy and value networks.mp4 47.7 MB
078. Chapter 13. Practical considerations for training your own AlphaGo.en.srt 5.4 KB
078. Chapter 13. Practical considerations for training your own AlphaGo.mp4 13.8 MB
079. Chapter 13. Summary.en.srt 1.7 KB
079. Chapter 13. Summary.mp4 5.6 MB
080. Chapter 14. AlphaGo Zero - Integrating tree search with reinforcement learning.en.srt 9.8 KB
080. Chapter 14. AlphaGo Zero - Integrating tree search with reinforcement learning.mp4 19.0 MB
081. Chapter 14. Guiding tree search with a neural network.en.srt 21.3 KB
081. Chapter 14. Guiding tree search with a neural network.mp4 28.8 MB
082. Chapter 14. Training.en.srt 6.7 KB
082. Chapter 14. Training.mp4 12.9 MB
083. Chapter 14. Improving exploration with Dirichlet noise.en.srt 4.7 KB
083. Chapter 14. Improving exploration with Dirichlet noise.mp4 9.5 MB
084. Chapter 14. Modern techniques for deeper neural networks.en.srt 6.5 KB
084. Chapter 14. Modern techniques for deeper neural networks.mp4 11.4 MB
085. Chapter 14. Exploring additional resources.en.srt 2.2 KB
085. Chapter 14. Exploring additional resources.mp4 5.3 MB
086. Chapter 14. Wrapping up.en.srt 1.2 KB
086. Chapter 14. Wrapping up.mp4 1.8 MB
087. Chapter 14. Summary.en.srt 1.7 KB
087. Chapter 14. Summary.mp4 4.7 MB
088. Appendix A. Mathematical foundations.en.srt 16.6 KB
088. Appendix A. Mathematical foundations.mp4 24.7 MB
089. Appendix B. The backpropagation algorithm.en.srt 12.8 KB
089. Appendix B. The backpropagation algorithm.mp4 21.2 MB
090. Appendix C. Go programs and servers.en.srt 7.0 KB
090. Appendix C. Go programs and servers.mp4 16.2 MB
091. Appendix D. Training and deploying bots by using Amazon Web Services.en.srt 18.9 KB
091. Appendix D. Training and deploying bots by using Amazon Web Services.mp4 33.8 MB
092. Appendix E. Submitting a bot to the Online Go Server.en.srt 17.9 KB
092. Appendix E. Submitting a bot to the Online Go Server.mp4 32.0 MB
Bonus Resources.txt 70 bytes

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