Want to Learn Deep Learning from MIT Without Leaving Your Browser? Here's How

You've watched the lectures, read the blog posts, maybe even bookmarked a dozen tutorials. But at some point you have to actually write code, and that's usually where self-study falls apart. MIT's Introduction to Deep Learning course solves that problem by putting its entire lab sequence on GitHub, ready to run in Google Colab with a GPU.

What It Does

This repository contains all the code and software labs for MIT Introduction to Deep Learning. The lecture slides and videos live on the program website, but the hands-on work — the part where you actually build things — is right here in the repo.

The labs are Jupyter notebooks organized into folders (lab1, lab2, lab3). They're designed to be completed at your own pace, and they run entirely in Google Colaboratory, which means you don't download anything and you don't configure a local environment. You just need a Google account. Each lab notebook has #TODO cells for you to fill in, so you're writing the code rather than watching someone else do it. At the end of each lab, there are instructions for submitting your work as part of the lab competitions.

The notebooks also install the mitdeeplearning Python package via pip — a set of convenience functions used throughout the course. That package is open source under the same license, so you can import it outside of class too:

import mitdeeplearning as mdl

Why It's Cool

• Zero setup friction. The single biggest reason people abandon online courses is environment problems — CUDA versions, driver mismatches, dependency hell. Running in Colab sidesteps all of it. You click a link, choose a runtime, and you're writing code in under a minute.

• Free GPU access. This is the part that matters for deep learning specifically. You don't need to own a GPU or rent cloud compute. In Colab, you go to Runtime, change the runtime type, select Python 3, and pick GPU as the hardware accelerator. For a course where you're training actual models, that's not a small convenience.

• Structure without hand-holding. The #TODO cells are a smart design choice. You're not copying and pasting finished code, and you're not staring at a blank notebook either. You get scaffolding — the surrounding code compiles, the shapes line up — and you fill in the parts that teach the concept.

• The package is reusable. mitdeeplearning isn't locked behind a course login or a Colab-only import. It's on the Python Package Index, open source, and installable anywhere. If the convenience functions are useful to you beyond the labs, you can keep using them.

• The lectures are public too. The README links to the full lecture video playlist on YouTube. So you can watch the theory and then immediately do the lab, without paying for anything or sitting through a paywalled platform.

One thing worth flagging: the code is licensed under MIT, but use and modification outside of the course requires attribution — you have to reference "© MIT Introduction to Deep Learning" and the program URL. That's a reasonable ask for free course material, just don't strip the credit.

How to Try It

1. Head to the repository: github.com/mitdeeplearning/introtodeeplearning
2. Navigate to the lab folder you want — lab1, lab2, or lab3 — and open the corresponding .ipynb notebook.
3. Click the "Run in Colab" link at the top of the lab.
4. In Colab, go to Runtime → Change runtime type. Under "Runtime type" select Python 3, and under "Hardware accelerator" select GPU.
5. Work through the notebook, filling in the #TODO cells to get the code compiling.

That's the whole setup. No virtualenv, no requirements.txt to debug, no GPU drivers.

If you want to use the package outside the labs:

pip install mitdeeplearning

And the lecture videos are linked from the README if you want the theory alongside the code.

Final Thoughts

This is best for someone who already has some Python and basic ML familiarity and wants structured, hands-on practice without the overhead of setting up a training environment. It's coursework, not a library — so if you're looking for a tool to drop into production, this isn't it. But as a way to actually train models on a GPU for free, with real assignments to complete, it's hard to beat. The labs are self-paced, so there's no deadline pressure and no reason not to open lab1 this week and see how far you get.

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