Intro to Deep Learning is a collection of educational lab materials and Jupyter notebooks for MIT's Introduction to Deep Learning course.
The repository addresses the need for hands-on deep learning education by providing structured software labs that teach neural networks, computer vision, music generation, and deep reinforcement learning. The labs are designed to be completed at your own pace using Google Colaboratory, a cloud-based Jupyter notebook environment that requires no local setup beyond a Google account. Students work through notebooks containing TODO cells that guide them through implementing deep learning concepts using TensorFlow and PyTorch. The repository also includes a companion Python package called mitdeeplearning that provides convenience functions used throughout the course.
This material suits anyone seeking a structured introduction to deep learning fundamentals, whether as a self-paced learner or as part of the MIT course itself. The labs progress through multiple modules covering different aspects of deep learning, and students can submit their completed work as part of lab competitions. The use of Google Colaboratory with GPU acceleration built in removes barriers to entry for those without local machine learning hardware. The accompanying lecture videos and slides provide theoretical context alongside the practical coding exercises.
The project maintains active lab materials with structured updates tied to course offerings. The codebase includes multiple lab modules organized in separate folders, each containing complete Jupyter notebooks with embedded instructions. The mitdeeplearning package is published to the Python Package Index and remains open source, allowing use beyond the course context. Documentation clearly specifies attribution requirements for use outside the official course.
