mitdeeplearning/introtodeeplearning

★ 8,639Jupyter NotebookAudience · researcherComplexity · 2/5LicenseSetup · easy

Mindmap

mindmap
  root((MIT DeepLearning Labs))
    What it does
      Course lab notebooks
      Guided coding
      TODO fill-in format
    Topics
      Computer vision
      Music generation
      Reinforcement learning
    Tech
      Python
      Jupyter Notebook
      Google Colab
    Audience
      Students
      Self-learners

mindmap root((MIT DeepLearning Labs)) What it does Course lab notebooks Guided coding TODO fill-in format Topics Computer vision Music generation Reinforcement learning Tech Python Jupyter Notebook Google Colab Audience Students Self-learners

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Things people build with this

USE CASE 1

Work through a guided deep learning lab on computer vision by opening a notebook in Google Colab and filling in the TODO code sections.

USE CASE 2

Learn how neural networks are trained by running the lab code step by step on free GPU hardware without installing anything locally.

USE CASE 3

Use the course notebooks as a structured starting point for building your own deep learning experiments after completing each guided section.

Tech stack

PythonJupyter NotebookGoogle Colab

Getting it running

Difficulty · easy Time to first run · 30min

No local installation needed, open the notebook link in Google Colab, select a GPU runtime, and run the cells.

MIT License, use and modify freely for any purpose as long as you credit MIT Introduction to Deep Learning when publishing or sharing externally.

In plain English

This repository holds the lab materials for MIT's Introduction to Deep Learning course, known as 6.S191. Deep learning is a branch of artificial intelligence where computer systems learn to recognize patterns by processing large amounts of data through layered mathematical structures called neural networks. The course is taught at MIT and covers topics including computer vision, music generation, and deep reinforcement learning. The labs are the hands-on coding portion of the course and are stored here as Jupyter notebooks, which are documents that mix explanatory text, code, and output in a format you can run interactively. The instructions say you do not need to install anything on your own computer because the labs are designed to run in Google Colaboratory, a free online service that lets you execute Python code in a browser using Google's servers, including access to graphics hardware that speeds up the training computations. To work through a lab, you open the notebook file from this repository, click a link to load it in Colaboratory, select a GPU as your hardware option in the settings, and then fill in the code sections marked with TODO comments. The course also publishes a companion Python package called mitdeeplearning that provides helper functions used across the labs, installable with a single command. All lecture slides and videos from the course are freely available at the course website and on YouTube. The code in this repository is published under the MIT License with a condition that any external use must credit MIT Introduction to Deep Learning. The audience is students and self-learners who want structured, practical experience with deep learning concepts through guided coding exercises.

Copy-paste prompts

Prompt 1

I am working through the MIT 6.S191 introtodeeplearning lab on computer vision. Explain what a convolutional neural network does in plain English and then help me fill in the TODO section that defines the model architecture.

Prompt 2

I got a shape mismatch error in the MIT deep learning lab notebook. Here is the error message and the relevant code cell, help me debug it.

Prompt 3

I finished the MIT 6.S191 music generation lab. Help me modify the trained RNN model to generate a longer sequence and change the temperature parameter to make the output more creative.

Prompt 4

Explain the difference between the loss functions used in the MIT introtodeeplearning labs for classification versus for the generative model lab.

Prompt 5

I want to adapt the MIT 6.S191 reinforcement learning lab to train an agent on a different OpenAI Gym environment. Show me which parts of the notebook code to change and what to replace them with.

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