Deep Learning
Fall Semester 2016
Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
In recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the fundamentals of deep learning and provide a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.
Course Catalogue InfoNews
11/08/2016 | The webpage for this year's course is online. |
27/09/2016 | Added slides lecture 1 + exercise 1. |
03/10/2016 | Added slides lecture 2 + exercise 2 |
10/10/2016 | Added slides lecture 3. |
10/10/2016 | Added exercise 3 + solutions for exercise 2. |
17/10/2016 | Added slides lecture 4. |
18/10/2016 | Added exercise 4 + solutions for exercise 3. |
24/10/2016 | Added slides lecture 5. |
25/10/2016 | Added exercise 5 + solutions for exercise 4. |
01/11/2016 | Added exercise 6 + solutions for exercise 5. |
08/11/2016 | Updated slides lecture 6 + added exercises 7 & solutions for exercise 6. |
14/11/2016 | Added slides lecture 7 + exercise 8. |
21/11/2016 | Added slides lecture 8. |
22/11/2016 | Added exercise 9. |
28/11/2016 | Updated lecture 8. |
18/01/2017 | Added sample questions for the exam. |
Course Overview
Week | Date | Topic | Slides | Exercises | Material |
---|---|---|---|---|---|
1 | 26.09.2016 | Feedforward networks: architecture | slides 1 | exercise 1 | |
2 | 03.10.2016 | Feedforward networks: learning | slides 2 | exercise 2 | solutions 2 |
3 | 10.10.2016 | Optimization for neural networks | slides 3 | exercise 3 | solutions 3 |
4 | 17.10.2016 | Regularization for neural networks | slides 4 | exercise 4 | solutions 4 |
5 | 24.10.2016 | Convolutional Neural Networks | slides 5 | exercise 5 | solutions 5 |
6 | 31.10.2016 | Recurrent and recursive neural networks | slides 6 |
exercise 6 TwoLayerCNN.py train_and_test_MNIST.py |
solutions 6 code solution |
7 | 07.11.2016 | Recurrent and recursive neural networks (part 2) |
exercise 7 | solutions 7 code solution |
|
8 | 14.11.2016 | Factor models, autoencoders, latent representations | slides 7 | exercise 8 | code solution |
9 | 21.11.2016 - 28.11.2016 | Undirected Deep Models & Generative models |
slides 8 | exercise 9 Template (.ipynb) Template (.py) |
solutions 9 |
- | 05.12.2016 | No lecture (project) | No exercise session | ||
- | 12.12.2016 | No lecture (project) | No exercise session | ||
- | 18.01.2017 | - | sample exam questions |
Contact
Lecturer | Prof. Thomas Hofmann |
Assistant | Aurelien Lucchi |
Assistant | Florian Schmidt |
Assistant | Yannic Kilcher |
Assistant | Gary Becigneul |
Assistant | Javier Montoya |
Literature
Deep Learning, An MIT Press book, Ian Goodfellow, Yoshua Bengio and Aaron Courville