https://github.com/lexfridman/mit-deep-learning

Tutorials, assignments, and competitions for MIT Deep Learning related courses.
https://github.com/lexfridman/mit-deep-learning

artificial-intelligence data-science deep-learning deep-reinforcement-learning deep-rl deeplearning jupyter-notebooks machine-learning mit neural-networks segmentation self-driving-cars tensorflow tensorflow-tutorials

Last synced: 27 days ago
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Tutorials, assignments, and competitions for MIT Deep Learning related courses.

• Host: GitHub
• URL: https://github.com/lexfridman/mit-deep-learning
• Owner: lexfridman
• License: mit
• Created: 2017-01-08T00:52:01.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2024-01-03T13:54:05.000Z (10 months ago)
• Last Synced: 2024-09-30T16:42:33.600Z (about 1 month ago)
• Topics: artificial-intelligence, data-science, deep-learning, deep-reinforcement-learning, deep-rl, deeplearning, jupyter-notebooks, machine-learning, mit, neural-networks, segmentation, self-driving-cars, tensorflow, tensorflow-tutorials
• Language: Jupyter Notebook
• Homepage: https://deeplearning.mit.edu
• Size: 62.4 MB
• Stars: 10,143
• Watchers: 643
• Forks: 2,210
• Open Issues: 14
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# MIT Deep Learning



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



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



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)



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



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/)