Skip to content
A deep learning workshop, done from scratch, taught without any frameworks.
Branch: master
Clone or download
Latest commit 3eb8354 Jul 12, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
data finished tutorial material Dec 24, 2018
figures final update Dec 25, 2018
notebooks scipy2019 closing commit Jul 12, 2019
scripts finished tutorial material Dec 24, 2018
.gitignore add instructions regarding jlab Jun 24, 2019
environment.yml added pip, jax, and jaxlib to environment spec Apr 18, 2019


In this workshop, I will build your intuition in deep learning, without using a framework.

Getting Started

You can get started using one of the following methods.

1. Setup using conda environments

$ conda env create -f environment.yml
$ conda activate dl-workshop  # older versions of conda use `source activate` rather than `conda activate`
$ python -m ipykernel install --user --name dl-workshop
$ jupyter labextension install @jupyter-widgets/jupyterlab-manager

If you want jax with GPU, you will need to build from source, or follow the installation instructions

2. "just click Binder"



If you are using Jupyter Lab, you will want to also ensure that ipywidgets is installed:

# only if you don't have ipywidgets installed.
$ conda install -c conda-forge ipywidgets
# the next line is necessary.
$ jupyter labextension install @jupyter-widgets/jupyterlab-manager

Key Ideas

The key idea for this tutorial is that if we really study deep learning's fundamental model, linear regression, then we can get a better understanding of the components - a model with parameters, a loss function, and an optimizer to change the parameters to minimize the loss. Most of us who become practitioners (rather than researchers) can then take for granted that the same ideas apply to any more complex/deeper model.


I'd love to hear how well this workshop went for you. Please consider leaving feedback so I can improve the workshop.

Further Reading:

You can’t perform that action at this time.