02456 Deep Learning (with PyTorch)

This repository contains exercises for the DTU course 02456 Deep Learning. All exercises are written in the Python programming language and formatted into Jupyter Notebooks. If you are unfamiliar with notebooks, it can be a good idea to familiarize yourself with them in advance.

This repository borrows heavily from previous works, in particular:

• 2015 DTU Summerschool in Deep Learning. A PhD summerschool that was held at DTU in 2015. Exercises both in numpy and Theano.

• 02456-deep-learning. Previous version of the course material for this course, but using TensorFlow for the exercises.

• Pytorch Tutorial. A remix popular deep learning materials, including material from 02456, collected in one coherent package using PyTorch, with a focus on natural language processing (NLP)

• pytorch/tutorials. Official tutorials from the PyTorch repo.

Setup

The recommended (and by far the easiest) way to get started with the exercises is by using Google Colab. It allows you to work with Jupyter Notebooks in the cloud with all pre-installed dependencies, and Colab offers free GPU access, enabling you to run the exercises considerably faster. Also, all exercises have been tested on Colab.

If you prefer to work locally, we recommend using either Anaconda (for both Linux, Mac, and Windows). On Linux, you can use the system Python, and on Mac, you can also use a newer version of Python through Homebrew.

To run on the DTU HPC, please see the guide on the course homepage.

Additional content

If you are interested in some PyTorch code bases check out the following links:

• PyTorch examples
• PyTorch tutorials
• Discuss PyTorch on the Forums