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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 synthesize artificial data using simulations to train traditional machine learning models. Thatâ s just the beginning.
With this practical book, youâ ll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating 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.
You'll learn how to:
- • Design an approach for solving ML and AI problems using simulations with the Unity engine • Use a game engine to synthesize images for use as training data • Create simulation environments designed for training deep reinforcement learning and imitation learning models • Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization • Train a variety of ML models using different approaches • Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits
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 synthesize artificial data using simulations to train traditional machine learning models. Thatâ s just the beginning.
With this practical book, youâ ll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating 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.
You'll learn how to:
- • Design an approach for solving ML and AI problems using simulations with the Unity engine • Use a game engine to synthesize images for use as training data • Create simulation environments designed for training deep reinforcement learning and imitation learning models • Use and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimization • Train a variety of ML models using different approaches • Enable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 331 |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9781492089926 |
| ISBN-10: | 1492089923 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Buttfield-Addison, Paris
Buttfield-Addison, Mars Nugent, Tim Manning, Jon |
| Hersteller: | O'Reilly Media |
| Maße: | 231 x 176 x 26 mm |
| Von/Mit: | Paris Buttfield-Addison (u. a.) |
| Erscheinungsdatum: | 12.07.2022 |
| Gewicht: | 0,706 kg |
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 331 |
| Inhalt: | Kartoniert / Broschiert |
| ISBN-13: | 9781492089926 |
| ISBN-10: | 1492089923 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Buttfield-Addison, Paris
Buttfield-Addison, Mars Nugent, Tim Manning, Jon |
| Hersteller: | O'Reilly Media |
| Maße: | 231 x 176 x 26 mm |
| Von/Mit: | Paris Buttfield-Addison (u. a.) |
| Erscheinungsdatum: | 12.07.2022 |
| Gewicht: | 0,706 kg |
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