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Overview
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...
Syllabus
Module 1 - Set up your environment
- Signing up for an IBM Cloud Account
- Installing Swift for TensorFlow
Module 2 - Understanding Swift's basics
- Why Swift & Swift for TensorFlow?
- Server-side Swift
Module 3 - Tic Tac Toe (Minimax)
- Why & how does minimax work?
- Lab: Implementing Minimax in Swift
Module 4 - Cartpole (Reinforcement Learning)
- What is Reinforcement Learning (RL)?
- Lab: Implementing RL for Cartpole in Swift with OpenAI Gym
Module 5 - 2048 (Monte Carlo Tree Search)
- Using the pre-built 2048 game
- Why & how does Monte Carlo Tree Search (MCTS) work?
- Lab: Implementing MCTS for 2048 in Swift
- Creating a Swift Runtime service instance on IBM Cloud
- Lab 4 - Deploying the AI-powered 2048 web app
Instructor
Tanmay Bakshi
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