PyTorch-Deep-Learning-Minicourse

Minicourse in Deep Learning with PyTorch

Table of contents

T: theory P: practice

1. T Learning paradigms: supervised-, unsupervised-, and reinforcement-learning
2. P Getting started with the tools: Jupyter notebook, PyTorch tensors and autodifferentiation
3. T+P Supervised learning: regression and classification
4. T+P Neural net's forward and backward propagation
5. T+P Convolutional, spectral, recurrent, and graph neural nets improve performance by exploiting data nature
6. T+P Unsupervised learning: vanilla and variational autoencoders, generative adversarial nets
7. T+P Fighting overfitting: regularisation and other techniques
8. T+P Case study: character- or word-level language modelling
9. P Case study: analysing time series data of variable length / Keras and TensorFlow

Sessions

1. Time slot 1 (1h30min + 45 min = 2h15min) on Tuesday afternoon
2. Time slot 2 (1h30min + 45 min = 2h15min) on Wednesday afternoon
3. Extra section (45min) on Thursday afternoon
4. Extra section (1h30min) on Friday morning

Notebooks visualisation

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 #181818 code block.

Getting started

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 following website: https://conda.io/miniconda.html 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:

conda --help

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

Start jupyter notebook

Finally, start the jupyter notebook, if working on laptop do:

jupyter notebook

If working in JupyterLab, open a Jupyter instance and select file with the first notebook, next change the kernel.

Make sure you switch to the codas-ml kernel before proceeding with the exercise. If you cannot find the kernel corresponding to the newly created Anaconda environment, exit the jupyter and do:

python -m ipykernel install --user --name codas-ml --display-name "Codas ML"
jupyter notebook #restart