9781492089926 - practical simulations for machine learning: using synthetic data for ai von buttfield-addison, paris; buttfield-addison, mars; nugent, tim; manning, jon (8 Ergebnisse)
Practical Simulations for Machine Learning: Using Synthetic Data for AI
Buttfield-Addison, Paris; Buttfield-Addison, Mars; Nugent, Tim; Manning, Jon
- Softcover
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Paperback. Zustand: 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.
- Softcover
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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes KönigreichPBShop.store UK
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EUR 44,39
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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Practical Simulations for Machine Learning: Using Synthetic Data for AI
Buttfield-Addison, Paris; Buttfield-Addison, Mars; Nugent, Tim; Manning, Jon
- Softcover
- Erstausgabe
Anbieter: PearlPress, Camperdown, NSW, AustralienPearlPress
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EUR 56,11
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Soft cover. Zustand: New. 1st Edition. 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.
Practical Simulations for Machine Learning: Using Synthetic Data for AI
Buttfield-Addison, Paris; Buttfield-Addison, Mars; Nugent, Tim; Manning, Jon
- Softcover
Anbieter: Majestic Books, Hounslow, Vereinigtes KönigreichMajestic Books
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EUR 70,41
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Zustand: New.
Practical Simulations for Machine Learning: Using Synthetic Data for Ai
Buttfield-addison, Paris/ Manning, Jon/ Buttfield-addison, Mars/ Nugent, Tim
- Softcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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EUR 65,54
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Paperback. Zustand: Brand New. 500 pages. 9.19x7.00x0.91 inches. In Stock.
- Softcover
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EUR 90,07
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Zustand: New. 2022. Paperback. . . . . . Books ship from the US and Ireland.
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 67,00
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Taschenbuch. Zustand: 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 d…ata 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.
