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Hands-On Deep Learning for Games

You're reading from   Hands-On Deep Learning for Games Leverage the power of neural networks and reinforcement learning to build intelligent games

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781788994071
Length 392 pages
Edition 1st Edition
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Author (1):
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Micheal Lanham Micheal Lanham
Author Profile Icon Micheal Lanham
Micheal Lanham
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics FREE CHAPTER
2. Deep Learning for Games 3. Convolutional and Recurrent Networks 4. GAN for Games 5. Building a Deep Learning Gaming Chatbot 6. Section 2: Deep Reinforcement Learning
7. Introducing DRL 8. Unity ML-Agents 9. Agent and the Environment 10. Understanding PPO 11. Rewards and Reinforcement Learning 12. Imitation and Transfer Learning 13. Building Multi-Agent Environments 14. Section 3: Building Games
15. Debugging/Testing a Game with DRL 16. Obstacle Tower Challenge and Beyond 17. Other Books You May Enjoy

Exercises

Use these additional exercises to assist in your learning and test your knowledge further.

Answer the following questions:

  1. Name three different activation functions. Remember, Google is your friend.
  2. What is the purpose of a bias?
  3. What would you expect to happen if you reduced the number of epochs in one of the chapter samples? Did you try it?
  4. What is the purpose of backpropagation?
  5. Explain the purpose of the Cost function.
  6. What happens when you increase or decrease the number of encoding dimensions in the Keras autoencoder example?
  7. What is the name of the layer type that we feed input into?
  8. What happens when you increase or decrease the batch size?
  9. What is the shape of the input Tensor for the Keras example? Hint: we already have a print statement displaying this.
  10. In the last exercise, how many MNIST samples do we train and test with?

As we progress in the book,...

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