Deep Learning (CAS machine intelligence)
This course in deep learning focuses on practical aspects of deep learning.
For the hands-on part we provide a docker container (details and installation instruction).
We took inspiration (and sometimes slides / figures) from the following resources.
Deep Learning Book (DL-Book) 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
The course is split in 8 sessions, each 4 hours long.
|Day||Topic and slides||Additional Material||Exercises and homework||1||
Deep learning basics slides
Multinomial Logistic Regression slides
||DL-book chapter 6|
Going Deeper / Tricks of the trade slides
Convolutional Neural Networks I
Convolutional Neural Networks II slides
Recurent Neural Networks slides
Un- and Semi-supervised Learning I slides
Un- and Semi-supervised Learning II slides