This course in deep learning focuses on practical aspects of deep learning. We therefore provide jupyter notebooks (complete overview of all notebooks used in the course).
For doing the hands-on part we recommend to use colab (you might need a google account) an internet connections is needed. If you want to do it without internet connection on your own computer you can either install anaconda (details and installation instruction) or use the provided a docker container (details and installation instruction).
To easily follow the course please make sure that you are familiar with the some basic math and python skills.
You can join together in small groups and choose a topic for your DL project. You should prepare a poster and a spotlight talk (5 minutes) which you will present on the last course day. To get some hints how to create a good poster you can check out the links that are provided in poster_guidelines.pdf
If you need free GPU resources, we might want to follow the instructions how to use google colab. Help for preparing a hdf5 file from your images you can be found in the example Notebook.
Examples for projects from the DL course 2018 and 2019 can be found here from 2020
Fill in the Title and the Topic of your Projects until 30.03.2021 here
We took inspiration (and sometimes slides / figures) from the following resources.
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Probabilistic Deep Learning (DL-Book) Probabilistic Deep Learning. This book is by the tensorchiefs and covers the increasingly popular probabilistic approach to deep learning .
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Deep Learning Book (DL-Book) http://www.deeplearningbook.org/. This is a quite comprehensive book which goes far beyond the scope of this course.
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Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/, has additional material and youtube videos of the lectures. While the focus is on computer vision, it also treats other topics such as optimization, backpropagation and RNNs. Lecture notes can be found at http://cs231n.github.io/.
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More TensorFlow examples can be found at dl_tutorial
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Another applied course in DL: TensorFlow and Deep Learning without a PhD
The course is split in 8 sessions, each 4 lectures long.
| Day | Date | Time |
|---|---|---|
| 1 | 23.02.2021 | 9:00-12:30 |
| 2 | 02.03.2021 | 9:00-12:30 |
| 3 | 09.03.2021 | 9:00-12:30 |
| 4 | 16.03.2021 | 9:00-12:30 |
| 5 | 23.03.2021 | 9:00-12:30 |
| 6 | 30.03.2021 | 9:00-12:30 |
| 7 | 06.04.2021 | 9:00-12:30 |
| 8 | 13.04.2021 | 9:00-12:30 |