introtodeeplearning

This repository holds the lab materials for MIT's Introduction to Deep Learning course, known as 6.S191. Deep learning is a branch of artificial intelligence where computer systems learn to recognize patterns by processing large amounts of data through layered mathematical structures called neural networks. The course is taught at MIT and covers topics including computer vision, music generation, and deep reinforcement learning.

The labs are the hands-on coding portion of the course and are stored here as Jupyter notebooks, which are documents that mix explanatory text, code, and output in a format you can run interactively. The instructions say you do not need to install anything on your own computer because the labs are designed to run in Google Colaboratory, a free online service that lets you execute Python code in a browser using Google's servers, including access to graphics hardware that speeds up the training computations.

To work through a lab, you open the notebook file from this repository, click a link to load it in Colaboratory, select a GPU as your hardware option in the settings, and then fill in the code sections marked with TODO comments. The course also publishes a companion Python package called mitdeeplearning that provides helper functions used across the labs, installable with a single command.

All lecture slides and videos from the course are freely available at the course website and on YouTube. The code in this repository is published under the MIT License with a condition that any external use must credit MIT Introduction to Deep Learning. The audience is students and self-learners who want structured, practical experience with deep learning concepts through guided coding exercises.