• Dresden


Florian Jug, Kashif Rasul, Peter Steinbach, others (DAIS/CIBI)

24.09.2018 – 28.09.2018

Center for Systems Biology Dresden / MPI-CBG

Registration website:

This hands-on course will take you from 0 to 100 in Deep Learning with Keras. Our aim is to teach the fundamentals of deep learning with Convolutional Neural Networks (CNN) based on modern techniques using the Keras API and the Tensorflow backend. By the end participants will know how to build deep learning models, how to train them, what to avoid during training, what to check during training and how to perform model inference, especially for image based problems. We hope participants will then go out and apply these methods to their own problems and use cases.

The core curriculum is planned from Monday (September 24) to Friday afternoon (September 28) to take place at the MPI CBG campus, Pfotenhauerstrasse 108, Dresden, Germany. As the agenda is currently being prepared please check-in from time to time.

All participants are expected to bring their laptop. During the workshop, a uniform access to GPU-enabled workstations or servers will be provided that hold the software stack used. Thus, your laptop is not required to hold a mobile GPU
or alike. All participants are expected to have a solid understanding of fundamentals of linear algebra as well as programming.

The workshop admission fee amounts to € 250 per participant. Every successful applicant is required to bring a poster to the workshop that describes their current scientific challenge that they would like to solve with Deep Learning. Posters have to be sent in 1 week prior to the workshop.

Learning goals:
- Fundamentals of deep learning with CNNs.
- Keras API with the Tensorflow backend.
- How to define your deep net.
- How to train it.

Bring your own laptop. Keras and Tensorflow backend should already be installed. (We will have GPU nodes you can use if your laptop does not offer a fast GPU.) Solid understanding of the fundamentals of linear algebra. Programming skills (never programmed… that will not work out, sorry!)

DeepLearning, Keras, Tensorflow, Python

Keras, Python, Tensorflow

Florian Jug (This email address is being protected from spambots. You need JavaScript enabled to view it.) and Peter Steinbach (This email address is being protected from spambots. You need JavaScript enabled to view it.)