# mit-deep-learning

**Repository Path**: jacklisp/mit-deep-learning

## Basic Information

- **Project Name**: mit-deep-learning
- **Description**: Tutorials, assignments, and competitions for MIT Deep Learning related courses.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-07-23
- **Last Updated**: 2021-11-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MIT Deep Learning

<a href="https://deeplearning.mit.edu/"><img src="https://deeplearning.mit.edu/files/images/mit_deep_learning.png"></a>

This repository is a collection of tutorials for [MIT Deep Learning](https://deeplearning.mit.edu/) courses. More added as courses progress.

## Tutorial: Deep Learning Basics

<a href="https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_deep_learning_basics/deep_learning_basics.ipynb"><img src="https://i.imgur.com/j4FqBuR.gif"></a>

This tutorial accompanies the [lecture on Deep Learning Basics](https://www.youtube.com/watch?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&v=O5xeyoRL95U). 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: \[ [Jupyter Notebook](https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_deep_learning_basics/deep_learning_basics.ipynb) \]
\[ [Google Colab](https://colab.research.google.com/github/lexfridman/mit-deep-learning/blob/master/tutorial_deep_learning_basics/deep_learning_basics.ipynb) \]
\[ [Blog Post](https://medium.com/tensorflow/mit-deep-learning-basics-introduction-and-overview-with-tensorflow-355bcd26baf0) \]
\[ [Lecture Video](https://www.youtube.com/watch?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&v=O5xeyoRL95U) \]


## Tutorial: Driving Scene Segmentation

<a href="https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb"><img src="images/thumb_driving_scene_segmentation.gif"></a>

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: \[ [Jupyter Notebook](https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb) \]
\[ [Google Colab](https://colab.research.google.com/github/lexfridman/mit-deep-learning/blob/master/tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb) \]

## Tutorial: Generative Adversarial Networks (GANs)

<a href="https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_gans/tutorial_gans.ipynb"><img src="images/thumb_mushroom_biggan.gif"></a>

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

Links: \[ [Jupyter Notebook](https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_gans/tutorial_gans.ipynb) \]
\[ [Google Colab](https://colab.research.google.com/github/lexfridman/mit-deep-learning/blob/master/tutorial_gans/tutorial_gans.ipynb) \]

## DeepTraffic Deep Reinforcement Learning Competition

<a href="https://selfdrivingcars.mit.edu/deeptraffic"><img src="images/thumb_deeptraffic.gif"></a>

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: \[ [GitHub](https://github.com/lexfridman/deeptraffic) \] \[ [Website](https://selfdrivingcars.mit.edu/deeptraffic) \] \[ [Paper](https://arxiv.org/abs/1801.02805) \]

## Team

- [Lex Fridman](https://lexfridman.com)
- [Li Ding](https://www.mit.edu/~liding/)
- [Jack Terwilliger](https://www.mit.edu/~jterwill/)
- [Michael Glazer](https://www.mit.edu/~glazermi/)
- [Aleksandr Patsekin](https://www.mit.edu/~patsekin/)
- [Aishni Parab](https://www.mit.edu/~aishni/)
- [Dina AlAdawy](https://www.mit.edu/~aladawy/)
- [Henri Schmidt](https://www.mit.edu/~henris/)