Practical Simulations for Machine Learning

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 Py Torch, and the Unity ML-Agents and Perception Toolkits Additional ISBNs9781492089896Practical Simulations for Machine Learning 1st Edition is written by Paris Buttfield-Addison; Mars Buttfield-Addison; Tim Nugent; Jon Manning and published by O’Reilly Media. ISBNs for Practical Simulations for Machine Learning are 9781492089872, 1492089877 and the print ISBNs are 9781492089926, 1492089923. Additional ISBNs include 9781492089896.