This repository contains exercises for the DTU continuing education course. All exercises are written in the Python programming language and formatted into Jupyter Notebooks. If you're 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:

• 02456 Deep Learning - Fall course in Deep Learning for postgraduates.

• 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.

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 dependencies pre-installed, and Colab offers GPU utility for free which allows you to run the exercises considerably faster.

You may also install Python locally using, e.g., using Anaconda.