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paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Practical Simulations for Machine Learning: Using Synthetic Data for AI
Published by O'Reilly Media (edition 1), 2022
ISBN 10: 1492089923 ISBN 13: 9781492089926
Language: English
Paperback. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Practical Simulations for Machine Learning: Using Synthetic Data for AI
Published by O'Reilly Media (edition 1), 2022
ISBN 10: 1492089923 ISBN 13: 9781492089926
Language: English
Paperback. Condition: Fair. 1. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.
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Paperback. Condition: New. 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 engineUse a game engine to synthesize images for use as training dataCreate simulation environments designed for training deep reinforcement learning and imitation learning modelsUse and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimizationTrain a variety of ML models using different approachesEnable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits.
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Practical Simulations for Machine Learning : Using Synthetic Data for Ai
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Practical Simulations for Machine Learning: Using Synthetic Data for AI (Paperback or Softback)
Published by O'Reilly Media 7/12/2022, 2022
ISBN 10: 1492089923 ISBN 13: 9781492089926
Language: English
Paperback or Softback. Condition: New. Practical Simulations for Machine Learning: Using Synthetic Data for AI 1.18. Book.
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Practical Simulations for Machine Learning
Published by O'Reilly Media, Inc, USA, 2022
ISBN 10: 1492089923 ISBN 13: 9781492089926
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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Practical Simulations for Machine Learning : Using Synthetic Data for Ai
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Practical Simulations for Machine Learning
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Add to basketPaperback. Condition: New. 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 engineUse a game engine to synthesize images for use as training dataCreate simulation environments designed for training deep reinforcement learning and imitation learning modelsUse and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimizationTrain a variety of ML models using different approachesEnable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits.
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Practical Simulations for Machine Learning: Using Synthetic Data for AI
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Practical Simulations for Machine Learning: Using Synthetic Data for AI
Published by O'Reilly Media, Inc, USA, 2022
ISBN 10: 1492089923 ISBN 13: 9781492089926
Language: English
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Practical Simulations for Machine Learning: Using Synthetic Data for Ai
Published by Oreilly & Associates Inc, 2022
ISBN 10: 1492089923 ISBN 13: 9781492089926
Language: English
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Add to basketPaperback. Condition: Brand New. 500 pages. 9.19x7.00x0.91 inches. In Stock.
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Add to basketPaperback. Condition: New. 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 engineUse a game engine to synthesize images for use as training dataCreate simulation environments designed for training deep reinforcement learning and imitation learning modelsUse and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimizationTrain a variety of ML models using different approachesEnable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits.
-
Practical Simulations for Machine Learning: Using Synthetic Data for AI
Published by O'Reilly Media, Inc, USA, 2022
ISBN 10: 1492089923 ISBN 13: 9781492089926
Language: English
Condition: New. 2022. Paperback. . . . . . Books ship from the US and Ireland.
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Practical Simulations for Machine Learning : Using Synthetic Data for AI
Published by O'reilly Media Jul 2022, 2022
ISBN 10: 1492089923 ISBN 13: 9781492089926
Language: English
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Add to basketTaschenbuch. Condition: Neu. Neuware - 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.
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Add to basketPaperback. Condition: New. 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 engineUse a game engine to synthesize images for use as training dataCreate simulation environments designed for training deep reinforcement learning and imitation learning modelsUse and apply efficient general-purpose algorithms for simulation-based ML, such as proximal policy optimizationTrain a variety of ML models using different approachesEnable ML tools to work with industry-standard game development tools, using PyTorch, and the Unity ML-Agents and Perception Toolkits.
