By combining a platform for creating and operating interactive, real-time 3D content with machine learning tools, you can use the 3D world you create to train a machine learning model, kind of like it's the real world. It's not actually like the real world, but it's fun to imagine, and there are some legitimately useful connections to the real world (such as being able to generate both data for use in real-world machine learning applications, as well as models that can be transposed to physical, real-world objects, like robots). Combining Unity with machine learning is a great way to create both simulations and synthetic data, which are the two different topics we cover in this book. We wrote this book for programmers and software engineers who are interested in machine learning, but are not necessarily machine learning engineers. If you have a passing interest in machine learning, or are starting to work more in the machine learning space, then this book is for you.

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 create artificial data using simulations to train traditional machine learning models. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, with a focus 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.