Game-playing AI with Swift for TensorFlow (S4TF)
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Learn how to accelerate machine learning model development with Google’s Swift for TensorFlow framework. Build AI agents to play games like Tic Tac Toe, Cartpole, and 2048. Discover the power of Swift for machine learning and its integration with the compiler. Train models faster with simpler code and gain a deeper understanding of AI agents in gaming. Set up your environment, understand Swift’s basics, and dive into modules focused on Tic Tac Toe, Cartpole, and 2048. Start your journey into the world of machine learning today.
At a Glance
In this course, you’re going to learn how to accelerate machine learning model development with Google’s new Swift for TensorFlow framework, by building AI agents to play games like Tic Tac Toe, Cartpole, and 2048.
Machine learning technology is one of the most exciting innovations of the past few years. It enables cars to drive themselves, and oncologists to diagnose cancer faster. However, from an implementation standpoint, machine learning has traditionally been difficult. This is because machine learning simply has different needs from other types of technologies – and programming languages, the infrastructure with which technology is built, weren’t designed with these special needs in mind.
However, Swift is the perfect language for the future of machine learning. The compiler is developed in a modular paradigm, and it has 2 intermediate stages in which code can be modified or injected. With Swift for TensorFlow, Google is showing their commitment to the world of machine learning. They’re integrating machine learning capabilities directly into the Swift language compiler. This enables you to write code naturally, and run tasks like automatic differentiation. You can even use Swift’s Control Flow (if, if-let, and guard statements, for/while loops, etc.)!
All in all, this means that you’ll be able to train machine learning models, faster, with less and simpler code, enabling a lower barrier of entry into this world. You’ll build a minimax agent for Tic Tac Toe, a Reinforcement Learning agent for Cartpole, and a Monte Carlo Tree Search agent for 2048! Upon completing this course, you’ll be able to understand the ideas behind Swift for TensorFlow, the basics of machine learning, and how AI agents are built to play games.
Module 1 – Set up your environment
Module 2 – Understanding Swift’s basics
Module 3 – Tic Tac Toe (Minimax)
Module 4 – Cartpole (Reinforcement Learning)
Module 5 – 2048 (Monte Carlo Tree Search)
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