Hands-On Deep Learning for Games
This is the code repository for Hands-On Deep Learning for Games, published by Packt.
Leverage the power of neural networks and reinforcement learning to build intelligent games
What is this book about?
The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development.
This book covers the following exciting features:
- Learn the foundations of neural networks and deep learning.
- Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots.
- Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems.
- Working with Unity ML-Agents toolkit and how to install, setup and run the kit.
- Understand core concepts of DRL and the differences between discrete and continuous action environments.
- Use several advanced forms of learning in various scenarios from developing agents to testing games.
If you feel this book is for you, get your copy today!
Instructions and Navigations
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)Following is what you need for this book: This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.
With the following software and hardware list you can run all code files present in the book (Chapter 1-13).
Software and Hardware List
| Chapter | Software required | OS required |
|---|---|---|
| 1-13 | Python 3.6 | Any |
| 4,6-12 | Unity | Windows or Mac OS X |
| 6-12 | ML-Agents Toolkit (Unity) | Windows or Mac OS X |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Get to Know the Author
Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas including games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.
Other books by the authors
- Learn ARCore - Fundamentals of Google ARCore
- Learn Unity ML-Agents - Fundamentals of Unity Machine Learning
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