Practical Simulations For Machine Learning Using Synthetic Data For Ai
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Practical Simulations for Machine Learning
Author: Paris Buttfield-Addison
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2022-06-07
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
Author: Paris Buttfield-Addison
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2022-06-07
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
Futureproofing Engineering Education for Global Responsibility
This book contains papers in the fields of: Virtual and augmented learning. Games in engineering education. Social aspects of digitalization. Technical teacher training. Accessible learning and technologies. Dance of data in educational science and practice. Engineering education for production and service structures of the future. Innovative approaches to STEAM education and music therapy through emerging technologies. We are currently witnessing a significant transformation in the development of education on all levels and especially in post-secondary and higher education. To face these challenges, higher education must find innovative and effective ways to respond in a proper way. Changes have been made in the way we teach and learn, including the massive use of new means of communication, such as videoconferencing and other technological tools.