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This repository is a collection of tutorials forMIT Deep Learning courses. More added as courses progress.

This tutorial accompanies thelecture on Deep Learning Basics. It presents several concepts in deep learning, demonstrating the first two (feed forward and convolutional neural networks) and providing pointers to tutorials on the others. This is a good place to start.

Links: [Jupyter Notebook ][Google Colab ][Blog Post ][Lecture Video ]

This tutorial demostrates semantic segmentation with a state-of-the-art model (DeepLab) on a sample video from the MIT Driving Scene Segmentation Dataset.

Links: [Jupyter Notebook ][Google Colab ]

This tutorial explores generative adversarial networks (GANs) starting with BigGAN, the state-of-the-art conditional GAN.

Links: [Jupyter Notebook ][Google Colab ]

DeepTraffic is a deep reinforcement learning competition. The goal is to create a neural network that drives a vehicle (or multiple vehicles) as fast as possible through dense highway traffic.

Links: [GitHub ] [Website ] [Paper ]

• Lex Fridman
• Li Ding
• Jack Terwilliger
• Michael Glazer
• Aleksandr Patsekin
• Aishni Parab
• Dina AlAdawy
• Henri Schmidt