This repository contains a reproducible course on the basics of deep learning.

Each topic is covered in a separate Jupyter notebook; each notebook contains theoretical introduction to its topic as well as a practical exercise.

1. Running the notebooks

For a general introduction to the Jupyter environment, I recommend this tutorial.

You can either run the Jupyter notebooks locally on your personal computer (see below for the installation instructions) or remotely with Jupyter Binder using the following link: https://mybinder.org/v2/gh/athms/deep-learning-basics/HEAD

2. Local installation

1. Clone and switch to this repository:

git clone https://github.com/athms/deep-learning-basics.git
cd deep-learning-basics

2. Install all dependencies listed in requirements.txt.

I recommend setting up a new Python environment (e.g., with the miniconda installer).

You can create a new conda environment using the following command:

conda create --name deep-learning-basics python=3.8

This will create a Python 3.8 environment with the name deep-learning-basics .

You can activate the new environment as follows:

conda activate deep-learning-basics

and then install all required dependencies with:

pip3 install -r requirements.txt

3. Start the Jupyter notebook server:

jupyter notebook

3. Course content

So far, this course covers the following topics:

• 0. Perceptrons and gradient descent
• 1. Neural networks and backpropagation
• 2. Computer vision with convolutional neural networks

I am hoping to expand on this over time, adding introductions to:

• Recurrent neural networks
• Explainable artificial intelligence
• Deep reinforcement learning
• Supervised, unsupervised, and self-supervised deep learning

4. Workshop requests

If you enjoyed the contents of this course and would like to host a workshop at your university (or other institution), email me at: athms.research@gmail.com