Summer School 2018
Deep Learning

General Information

Machine learning has become a part in our everydays life, from simple product recommendations to personal electronic assistant to self-driving cars. More recently, through the advent of potent hardware and cheap computational power, “Deep Learning” has become a popular and powerful tool for learning from complex, large-scale data.

In this course, we will discuss the fundamentals of deep learning and its application to various different fields. We will learn about the power but also the limitations of these deep neural networks. At the end of the course, the students will have significant familiarity with the subject and will be able to apply the learned techniques to a broad range of different fields.

Mainly, the course will cover practical as well theoretical aspects of the Deep Learning and also gives a broad general introduction to machine learning and statistical learning in general.

Materials

The students should bring:
  • You only need a laptop with a 64 Bit CPU and Python 3+ running
  • We will form teams and work on the practical parts of the course together, so even if your laptop is very old you can easily participate within a group
Reading Material (please note that the books cover the stuff in more detail than needed for the course):

Lectures

# Content Details Downloads
Day 1 Welcome Day - Introduction to the Course
- Group Building
- Setup of the systems
Slides
Intro to Python
Intro to numpy
Day 2 Mathematical Background - Linear Algebra
- Introduction to Statistics
- Exercises
Slides LinAlg
PCA
Statistic
Day 3 Introduction to Machine Learning - Definitions
- Training Approaches
- Overfitting/Underfitting
- Cross-Validation
- Linear Regression
ML Basics
Linear Regression
Day 4 Applied Statistical Learning - Logistic Regression
- Feed Forward Networks
- Exercises
Log Res
FFN
Day 5 Introduction to Neural Networks - Loss Functions
- Gradient Based Learning
- Backpropagation
Backpropagation
Regularization
Day 6 KERAS - Recap of 1st Week
- Regularization
- Introduction to KERAS
- Exercises
Intro to Keras
Day 7 CVV - Introduction to CVV
- Mini Project
CNN
Day 8 RNN - Introduction to RNN
- Mini Project
RNN
Example: Weather Data
Data
Day 9 Exam Preparation - Recap coming soon ...
Day 10 Exam - Exam

Exercises

# Content Downloads Reading Solution
1 Linear Algebra Exercise
Dataset
Deep Learning Book, Chapter 2 & 3 (except 2.8, 2.9, 3.13, 3.14) Solution
2 Linear Regression Exercise
Dataset
An Introduction to Statistical Learning, Chapter 2 & 3 Solution
3 Linear Regression Exercise
Dataset
An Introduction to Statistical Learning, Chapter 4.1, 4.2, 4.3 Solution using sklearn
Solution using statsmodels
4 Feed Forward Networks Exercise
Example Logistic Regression
Example Regression
Deep Learning Book, Chapter 6 Solution
5 Feed Forward Networks Exercise
cats-vs-dogs
Example ConvNet
Solution
6 Feed Forward Networks Exercise