Deep Learning (CAS machine intelligence)
This course in deep learning focuses on practical aspects of deep learning.
For the handson part we provide a docker container (details and installation instruction).
Other resources
We took inspiration (and sometimes slides / figures) from the following resources.

Deep Learning Book (DLBook) http://www.deeplearningbook.org/. This is a quite comprehensive book which goes far beyond the scope of this course.

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/.

More TensorFlow examples can be found at dl_tutorial

Another applied course in DL: TensorFlow and Deep Learning without a PhD
Syllabus
The course is split in 8 sessions, each 4 hours long.
Tensorchiefs are Oliver Dürr, Beate Sick and Elvis Murina.Day  Topic and slides  Additional Material  Exercises and homework 

1 
Deep learning basics slides



2 
Multinomial Logistic Regression slides

DLbook chapter 6 

3 
Going Deeper / Tricks of the trade slides



4 
Convolutional Neural Networks I
slides



5 
Convolutional Neural Networks II slides


6 
Recurent Neural Networks slides



7 
Un and Semisupervised Learning I slides


8 
Un and Semisupervised Learning II slides

