Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.
Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome!
This repository contains all of the code and software labs for MIT 6.S191: Introduction to Deep Learning! All lecture slides and videos are available on the course website. http://introtodeeplearning.com/
Opening the labs in Google Colaboratory: The 2021 6.S191 labs will be run in Google's Colaboratory, a Jupyter notebook environment that runs entirely in the cloud, you don't need to download anything. To run these labs, you must have a Google account.
On this Github repo, navigate to the lab folder you want to run (lab1, lab2, lab3) and open the appropriate python notebook (*.ipynb). Click the "Run in Colab" link on the top of the lab. That's it!
Running the labs Now, to run the labs, open the Jupyter notebook on Colab. Navigate to the "Runtime" tab --> "Change runtime type". In the pop-up window, under "Runtime type" select "Python 3", and under "Hardware accelerator" select "GPU". Go through the notebooks and fill in the #TODO cells to get the code to compile for yourself!
MIT Deep Learning package You might notice that inside the labs we install the mitdeeplearning python package from the Python Package repository:
pip install mitdeeplearning
This package contains convienence functions that we use throughout the course and can be imported like any other Python package.
import mitdeeplearning as mdl
We do this for you in each of the labs, but the package is also open source under the same license so you can also use it outside the class.