There are three different ways to follow the course for the hands-on part. You're recommended to use the Jupyter Lab but other backup options are also available (i.e., Google Colab and HPC2N).

How to install: see README.

See Howto_GoogleColab.

See instructions for how to install and run from command line (no Jupyter Lab) at HPC2N.

We mostly will work with:

• The MNIST dataset for image processing
• The IMDB reviews for NLP tasks.

• Tensorflow and Tensorboard: basics
• Tensorflow: Linear Regression with constraints during optimization.
• RNN: Language Modelling task.
• ETNLP: Exploring different word embeddings.
• Transformers: sentiment classification tasks.
• ELMO and BERT: sentiment classification tasks.
• GANs: Generative Adversarial Networks