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Deep Learning with Tensorflow

Instructor: Christoph Lampert

Teaching Assistant: Alexander Kolesnikov, Amelie Royer

Time and Location:

* Tuesday lectures: 13:15-14:30 (large seminar room, ground floor / Lab Bldg West)

* Thursdays hands-on sessions: 13:15-14:30 (Mondi 2 / Main Bldg)

Duration: Tue, 28-Nov-2017 to Thu, 25-Jan-2018

Pre-Meeting: 14-Nov-2017, 13:15-14:00, Mondi 2

Registration: [for external participants]   [for internal participants]




Recent years have seen a revival of artificial neural networks for machine learning and data analysis under the name of “Deep Learning”. Tensorflow is one of the leading programming environments for deep learning models. The course will start by giving an introduction into the current state of deep learning. Afterwards, participants will introduce different models in seminar-like talks. A large part of the course will be hands-on homework, where participants implement the described models and apply them to real data.


To benefit from the course, you will need

  • the willingness and ability to spend several hours per week on implementing and running code (computational resources will be provided)

  • the ability to use IST's git server (easy to learn, but won't be covered in the course)

To pass the course, you will need to

  • regularly attend in the course

  • give a 30 minute presentation about a deep learning model

  • upload implementations of hands-on homework to the IST gitlab server

There will be no final exam.



Final Grade



Schedule (tentative)

Date Topic Talk Title Presenter TA References (tentative)
Tue 14-Nov-2017 pre-meeting introduce format and distribute talk topics      
Tue 28-Nov-2017 Introduction to tensorflow Computation graphs / Linear Regression Christoph Lampert   [1] [2]
Tue 28-Nov-2017   presentation slides (graphs work only with Chrome)
complete (HTML) (don't cut-and-paste, code it yourself)
Christoph Lampert   Hands-On book chapter 9
Thu 30-Nov-2017   Hands-on session
Saving and restoring graphs (ipynb) (html)
Placing variables on CPU/GPU manually (ipynb)
Tue 5-Dec-2017 Artificial neural networks Multilayer Perceptrons (slides) Wojciech Rzadkowski Amelie Hands-On book chapter 10
Tue 5-Dec-2017   word2vec (slides) Antoine Karam Amelie [1] [2] [3] [4]
Thu 7-Dec-2017   Hands-on session
Using Tensorboard (ipynb) (html)
Variable scopes (ipynb)
Tue 12-Dec-2017 Convolutional neural networks 1 Convolutional neural networks (ConvNet demo) (slides) Dominik Schröder Alex Hands-On book chapter 13
Tue 12-Dec-2017   AlexNet (slides) Djordje Slijepcevic Alex [1]
Thu 14-Dec-2017   hands-on session      
Tue 19-Dec-2017 Convolutional neural networks 2 Modern ConvNet architectures (slides) Nikola Konstantinov Alex [1] [2]
Tue 19-Dec-2017   Neural networks tricks-of-the-trade Daniel Boocock Alex Hands-On book chapter 11
  holiday break        
Tue 9-Jan-2018 Deep generative models Variational Autoencoders (slides) Georg Sperl Amelie Hands-On book chapter 15, [1] [2] [3]
Tue 9-Jan-2018   Generative Adversarial Networks (slides) Mary Phuong Amelie [1] [2]
Thu 11-Jan-2018   hands-on session      
Tue 16-Jan-2018 Deep reinforcement learning Deep Q-Learning Valentyn Boreiko Alex Hands-On book chapter 16, [1] [2 (the DQN part)]
Tue 16-Jan-2018   Playing Atari with Deep Reinforcement Learning Patrick Blies Alex [1] [2]
Thu 18-Jan-2018   hands-on session      
Tue 23-Jan-2018 time to complete homework        
Thu 25-Jan-2018 time to complete homework        


File Due Date
HW1: Logistic Regression (Git page) PDF Dec 5th 2017
HW2: Multilayer Perceptron (Git page) PDF Dec 12th 2017
Results: bphuong 98.0, bvalentyn 97.7, nkonstan 97.5, dschroed 97.3, gsperl 97.2, dslijepc 97.2, vtoman 97.1, dboocock 97.0, koelsboe 96.8, wrzadkow 96.7  
HW3: Convolutional Neural Networks (Git page) PDF Dec 19th 2017
Results: nkonstan 99.0 (+1.5), bvalentyn 99.0 (+1.3), dboocock 99.0 (+1.9), dslijepc 99.0 (+1.8), dschroed 98.9 (+1.6), gsperl 98.9 (+1.7), bphuong 98.9 (+0.9), wrzadkow 98.3 (+1.7), koelsboe 98.3 (+1.5), vtoman 98.0 (+0.9)  
HW4: Modern ConvNets (Git page) PDF Jan 9th 2018
Results: nkonstan 66.6, gsperl 65.2, bphuong 63.6, dboocock 62.6, dslijepc 56.2, wrzadkow 52.2, dschroed 51.4, bvalentyn 23.4 42.3, koelsboe 2.7 22.8  
HW5: Image Generation (Git page) PDF Jan 16th 2018
Results: VAE-bphuong.png VAE-dboocock.png VAE-dschroed.png VAE-dslijepc.png VAE-gsperl.png VAE-nkonstan.png  
HW6: Reinforcement learning (Git page) PDF Jan 23rd/30th 2018