Book description
Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can create artificial data using simulations to train traditional machine learning models. That's just the beginning.
With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, with a focus on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential.
With this deeply practical book, you'll learn how to:
- Design an approach for solving ML and AI problems using simulations
- Use a game engine to synthesize images for use as training data
- Create simulation environments designed for training deep reinforcement learning and imitation learning
- Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization (PPO) and soft actor-critic (SAO)
- Train ML models locally, concurrently, and in the cloud
- Use PyTorch, TensorFlow, the Unity ML-Agents and Perception Toolkits to enable ML tools to work with industry-standard game development tools
Publisher resources
Table of contents
- Preface
- I. The Basics of Simulation and Synthesis
- 1. Introducing Synthesis and Simulation
- 2. Creating Your First Simulation
- 3. Creating Your First Synthesised Data
- II. Simulating Worlds for Fun and Profit
- 4. Creating a More Advanced Simulation
- 5. Creating a Self-Driving Car
- 6. Introducing Imitation Learning
- 7. Advanced Imitation Learning
- 8. Introducing Curriculum Learning
- 9. Cooperative Learning
- 10. Using Cameras in Simulations
- 11. Working with Python
- 12. Under the Hood and Beyond
- III. Synthetic Data, Real Results
- 13. Creating More Advanced Synthesized Data
- 14. Synthetic Shopping
- About the Authors
Product information
- Title: Practical Simulations for Machine Learning
- Author(s):
- Release date: June 2022
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492089926
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