Practical Simulations for Machine Learning: Using Synthetic Data for AI

Buttfield-Addison, Paris; Buttfield-Addison, Mars; Nugent, Tim; Manning, Jon

ISBN 10: 1492089923 / ISBN 13: 9781492089926

Editore: O'Reilly Media, 2022

Riguardo questo articolo

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. That's just the beginning. With this practical book, you'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.

Codice articolo ABE-1761254977004

Dati bibliografici

• Titolo: Practical Simulations for Machine Learning: Using Synthetic Data for AI
• Editore: O'Reilly Media
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1492089923
• ISBN 13: 9781492089926
• Rilegatura: Soft cover
• Edizione: 1st Edition
• Condizione: New

Descrizione articolo

Riassunto

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. That's just the beginning.

With this practical book, you'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

Informazioni sull?autore

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