Minicourse in Deep Learning with PyTorch
These lessons, developed during the course of several years while I've been teaching at Purdue and NYU, are here proposed for the Computational and Data Science for High Energy Physics (CoDaS-HEP) summer school at Princeton University. I'll upload the videos and link to them as soon as they are made available to me. I'm also planning to record them in a more quiet environment and at a slower pace, add them to my YouTube channel, and made available here.
Table of contents
TLearning paradigms: supervised-, unsupervised-, and reinforcement-learning
PGetting started with the tools: Jupyter notebook, PyTorch tensors and autodifferentiation
T+PNeural net's forward and backward propagation for classification
T+PConvolutional neural nets improve performance by exploiting data nature
T+PUnsupervised learning: vanilla and variational autoencoders, generative adversarial nets
T+PRecurrent nets natively support sequential data
- Time slot 1 (1h30min + 45 min = 2h15min) on Tuesday afternoon (1, 2, 3)
- Time slot 2 (1h30min + 45 min = 2h15min) on Wednesday afternoon (4)
- Extra section (45min) on Thursday afternoon (5)
- Extra section (1h30min) on Friday morning (6)
Jupyter Notebooks are used throughout these lectures for interactive data exploration and visualisation.
I use dark styles for both GitHub and Jupyter Notebook.
You better do the same, or they will look ugly.
To see the content appropriately install the following:
- Jupyter Notebook dark theme;
- GitHub dark theme and comment out the
invert #fff to #181818code block.
Keeping in touch
To be able to follow the workshop exercises, you are going to need a laptop with Miniconda (a minimal version of Anaconda) and several Python packages installed.
Following instruction would work as is for Mac or Ubuntu linux users, Windows users would need to install and work in the Gitbash terminal.
Download and install Miniconda
Please go to the Anaconda website.
Download and install the latest Miniconda version for Python 3.6 for your operating system.
wget <http:// link to miniconda> sh <miniconda .sh>
After that, type:
and read the manual.
Check-out the git repository with the exercise
Once Miniconda is ready, checkout the course repository and and proceed with setting up the environment:
git clone https://github.com/Atcold/PyTorch-Deep-Learning-Minicourse
If you do not have git and do not wish to install it, just download the repository as zip, and unpack it:
wget https://github.com/Atcold/PyTorch-Deep-Learning-Minicourse/archive/master.zip #For Mac users: #curl -O https://github.com/Atcold/PyTorch-Deep-Learning-Minicourse/archive/master.zip unzip master.zip
Create isolated Miniconda environment
Change into the course folder, then type:
#cd PyTorch-Deep-Learning-Minicourse conda env create -f conda-envt.yml source activate codas-ml
Enable anaconda kernel in Jupyter
To make newly created miniconda environment visible in the Jupyter, install
python -m ipykernel install --user --name codas-ml --display-name "Codas ML"
Start jupyter notebook
If you are working in a JupyterLab container double click on "Files" tab in the upper right corner.
Locate first notebook, double click to open.
Do not attempt to start
jupyter from the terminal window.
If working on a laptop, start from terminal as usual:
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