Guide · Open source
How to use mit-deep-learning: tutorials, notebooks and setup
Updated · written with AI from the project's own documentation and checked against our catalogue data
mit-deep-learning is a GitHub repository of hands-on tutorials that accompany MIT Deep Learning courses, mostly delivered as Jupyter notebooks. It suits students following those courses, beginners who want a guided first look at neural networks, and developers who learn better by running code. By the end of this guide you will know how to get the repository, which tutorials it contains, and how to open each one in Jupyter or Google Colab.
MIT Deep Learninglexfridman/mit-deep-learning · ★ 10.5k · MIT Install options & downloads Quick start
- 1Check prerequisites. Install Python and Jupyter, or prepare a Google account for Colab. The README does not state a Python version.
- 2Clone the repository. Run git clone https://github.com/lexfridman/mit-deep-learning in your terminal.
- 3Pick a tutorial. Start with the Deep Learning Basics notebook, which the README calls a good place to start.
- 4Open the notebook. Open the .ipynb file in Jupyter, or use the Google Colab link in the README.
- 5Run and modify the cells. Execute the cells in order, then change parameters and re-run to see the effect.
- 6Watch the lecture. Follow the linked MIT lecture video for each topic to match the code with the course material.
What is mit-deep-learning?
The repository is a collection of tutorials for MIT Deep Learning courses, and more are added as the courses progress. It is released under the MIT licence and its code is written mainly in Jupyter Notebook.
It is teaching material, not a full textbook. Use it alongside the course lectures or as a guided introduction to specific topics.
Install mit-deep-learning by cloning the repository
The README's install method is to clone the repository with git. Python is required, but the README does not state a version. You also need Jupyter, or a Google account if you want to use Colab instead.
- Make sure git is installed on your computer.
- Run the clone command below in the folder where you want the project to live.
- Open the cloned folder and find the tutorial folder you want.
git clone https://github.com/lexfridman/mit-deep-learningWhich tutorials does the repository include?
The README lists three tutorials, each with its own notebook. Deep Learning Basics is the recommended starting point. It presents several concepts and demonstrates the first two, feed forward and convolutional neural networks, while pointing to other tutorials for the rest.
Driving Scene Segmentation demonstrates semantic segmentation with the DeepLab model on a sample video from the MIT Driving Scene Segmentation Dataset. The GANs tutorial explores generative adversarial networks, starting with BigGAN.
- Deep Learning Basics: tutorial_deep_learning_basics/deep_learning_basics.ipynb
- Driving Scene Segmentation: tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb
- GANs: tutorial_gans/tutorial_gans.ipynb
Run a tutorial in Jupyter or Google Colab
After cloning, open the notebook for the tutorial you want in Jupyter. If you do not want to set up a local environment, the README also links each notebook to Google Colab, which runs it in your browser with a Google account.
Each tutorial also links to its lecture video, and the Deep Learning Basics tutorial links to a blog post. Watch the lecture alongside the notebook for context.
- Open the notebook in Jupyter for a local run.
- Open the Google Colab link to run it in the browser.
- Change a cell and re-run it to see how the output changes.
Try the DeepTraffic competition
DeepTraffic is a deep reinforcement learning competition in the same repository family. The goal is to create a neural network that drives a vehicle, or several vehicles, as fast as possible through dense highway traffic.
The README links to the DeepTraffic GitHub repository, the competition website and a paper. Check those pages for how the contest works, since the mit-deep-learning README does not describe it in detail.
- GitHub: github.com/lexfridman/deeptraffic
- Website: selfdrivingcars.mit.edu/deeptraffic
What people use it for
- Following along with an MIT Deep Learning course by running its tutorial notebooks.
- Getting a first hands-on look at feed forward and convolutional neural networks.
- Experimenting with DeepLab semantic segmentation on a driving video sample.
- Exploring GANs by starting from the BigGAN tutorial.
- Reading and reusing the DeepTraffic competition links to learn reinforcement learning for driving.
Frequently asked questions
Is mit-deep-learning free to use?
The repository is released under the MIT licence, as stated in the project details. Check the LICENSE file in the repository for the full terms.
What do I need to run mit-deep-learning?
The README lists Python, with no version stated, plus Jupyter or a Google account for Google Colab. You clone the repository with git to get the files.
Can I run the tutorials without installing anything?
Yes, each tutorial has a Google Colab link in the README that opens the notebook in your browser. You need a Google account to use Colab.
Is mit-deep-learning good for beginners?
The README describes the Deep Learning Basics tutorial as a good place to start, and it covers feed forward and convolutional neural networks. It is a guided first look rather than a complete course.
What are alternatives to mit-deep-learning?
Comparable projects include fast.ai course materials, Stanford CS231n assignments and Dive into Deep Learning. Choose one based on whether you want MIT course material or a broader reference.
Is mit-deep-learning suitable for production code?
No. The README describes teaching material and does not address production use, so treat the code as learning examples.
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Our verdict
Use mit-deep-learning if you are following MIT Deep Learning courses or want to learn neural networks by running notebooks. Pick something else if you need a general reference book or production-ready code, and consider fast.ai, CS231n or Dive into Deep Learning for broader coverage.
Install options & downloads