Motivation

Python is a general-purpose, versatile and popular programming language, that is becoming increasingly popular in science , for machine learning application and for general scripting and data analysis purposes. It’s great as a first language because it is concise and easy to read. However, finding motivation to learn Python or even to consolidate learned skills after joining a workshop, can be hard.

Course Details:

This course will be an introduction to deep learning. We will talk about different concepts, neural network (NN) architectures and their respective implementations in Python. Concepts and algorithms are of course library independent, nevertheless we will mainly focus on using the Keras Framework (part of Tensorflow) to implement different models. After some theoretical discussion we will setup together the deep learning environment and try to solve different problems using different NN architectures. 

Requirements

You should already feel quite comfortable in programming in python and bring some prior knowledge in how to work with pandas/numpy if possible. I would recommend to participate in the  “Scikit-Learn: Using Machine Learning in Python” courses if you are not comfortable with machine learning at all yet. I would advise to use a computer with a NVIDIA GPU to speed up computaions, but this is not mandatory. 

Correspondence:

For questions write to:

Stanislav Sys – stsys@uni-mainz.de or Stephan Weißbach – s.weissbach@uni-mainz.de (regarding Content)

Nicolas Ruffini Nicolas.ruffini@lir-mainz.de (regarding Event)

Registration:

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