# mitdeeplearning/introtodeeplearning

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8,702 stars · 4,540 forks · Jupyter Notebook · MIT

## Links

- GitHub: https://github.com/MITDeepLearning/introtodeeplearning
- awesome-repositories: https://awesome-repositories.com/repository/mitdeeplearning-introtodeeplearning.md

## Topics

`computer-vision` `deep-learning` `deep-reinforcement-learning` `deeplearning` `jupyter-notebooks` `mit` `music-generation` `neural-networks` `pytorch` `pytorch-tutorial` `tensorflow` `tensorflow-tutorials`

## Description

This repository contains the lab materials and Jupyter notebooks for MIT's introductory deep learning course, using TensorFlow and Keras for hands-on exercises. The courseware is delivered as pre-configured notebooks that run on Google Colaboratory's cloud infrastructure, eliminating the need for local software installation.

Learners can toggle the Colab runtime to a GPU-backed hardware accelerator for faster neural network training during lab exercises. A shared Python package provides helper functions that standardize common operations across all notebooks. The course guides students through a defined sequence of steps to format and submit completed lab work for course-hosted deep learning competitions.

## Tags

### Education & Learning Resources

- [Deep Learning Courses](https://awesome-repositories.com/f/education-learning-resources/deep-learning-courses.md) — Provides hands-on deep learning exercises in a structured course setting with pre-configured notebooks and automated grading support.
- [Jupyter Notebook Curricula](https://awesome-repositories.com/f/education-learning-resources/jupyter-notebook-curricula.md) — Delivers a structured deep learning curriculum as pre-configured Jupyter notebooks with embedded exercises.

### Part of an Awesome List

- [Deep Learning Labs](https://awesome-repositories.com/f/awesome-lists/learning/training-and-labs/deep-learning-labs.md) — Opens pre-configured Jupyter notebooks in Google Colaboratory so learners can complete exercises without local setup. ([source](https://cdn.jsdelivr.net/gh/mitdeeplearning/introtodeeplearning@master/README.md))
- [Course Competition Submissions](https://awesome-repositories.com/f/awesome-lists/devtools/competition-submission-tools/course-competition-submissions.md) — Guides learners through a defined sequence of steps to format and submit completed lab work for course competitions.

### Development Tools & Productivity

- [Managed Cloud Notebooks](https://awesome-repositories.com/f/development-tools-productivity/computational-notebooks/deep-learning-notebooks/managed-cloud-notebooks.md) — Opens and runs Jupyter notebooks in Google Colaboratory without local setup, enabling remote deep learning practice.

### DevOps & Infrastructure

- [Cloud Execution Environments](https://awesome-repositories.com/f/devops-infrastructure/cloud-execution-environments.md) — Provides pre-configured Jupyter notebooks that run entirely on Google Colaboratory's cloud infrastructure.

### Artificial Intelligence & ML

- [GPU Acceleration](https://awesome-repositories.com/f/artificial-intelligence-ml/gpu-acceleration.md) — Lets learners toggle the Colab runtime to a GPU hardware accelerator for faster neural network training.
- [GPU-Accelerated Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/gpu-accelerated-training.md) — Switches Colab runtimes to GPU hardware for faster neural network training during lab exercises.