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

Edité par O'Reilly Media, 2022

A propos de cet article

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.

N° de réf. du vendeur ABE-1761254977004

Détails bibliographiques

• Titre: Practical Simulations for Machine Learning: Using Synthetic Data for AI
• Éditeur: O'Reilly Media
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 1492089923
• ISBN à 13 chiffres: 9781492089926
• Reliure: Soft cover
• Édition: 1st Edition
• État de l'article: New

A propos de ce titre

Synopsis

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

À propos des auteurs

Dr. Paris Buttfield-Addison is cofounder of Secret Lab (https: //www.secretlab.com.au and @TheSecretLab on Twitter), a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award winning ABC Play School iPad games, Night in the Woods, the Qantas airlines Joey Playbox games, and the Yarn Spinner narrative game framework. Paris formerly worked as mobile product manager for Meebo (acquired by Google), has a degree in medieval history, a PhD in Computing, and writes technical books on mobile and game development (more than 20 so far) for O'Reilly Media. Paris particularly enjoys game design, statistics, law, machine learning, and human-centred technology research. He can be found on Twitter at @parisba and online at http: //paris.id.au.

Mars Buttfield-Addison is a computer science and machine learning researcher, as well as freelance creator of STEM educational materials. She is currently working toward her PhD in computer engineering at the University of Tasmania, collaborating with CSIRO's Data61 to investigate how large radio telescope arrays can be adapted to identify and track space debris and satellites in the near field while simultaneously performing deep space observations for astronomy. Mars can be found on Twitter @TheMartianLife and online at https: //themartianlife.com.

Dr. Tim Nugent pretends to be a mobile app developer, game designer, tools builder, researcher, and tech author. When he isn't busy avoiding being found out as a fraud, he spends most of his time designing and creating little apps and games that he won't let anyone see. Tim spent a disproportionately long time writing this tiny little bio, most of which was spent trying to stick a witty sci-fi reference in, before he simply gave up. Tim can be found on Twitter at @The_McJones, and online at http: //lonely.coffee.

Dr. Jon Manning is the cofounder of Secret Lab, an independent game development studio. He's written a whole bunch of books for O'Reilly Media about Swift, iOS development, and game development, and has a doctorate about jerks on the internet. He's currently working on Button Squid, a top-down puzzler, and on the critically acclaimed award winning adventure game Night in the Woods, which includes his interactive dialogue system Yarn Spinner. Jon can be found on Twitter at @desplesda, and online at http: //desplesda.net.