High-Performance Deep Learning

What?

On day 1 of this course you will:

• Understand the fundamental theories of machine learning and the intuitions/ideas behind the algorithms
• Work with a high-level machine learning API (Keras)
• Explore hyperparameter space to improve a neural network
• Understand the pitfalls of classic machine learning algorithms

On day 2 of this course you will learn:

• How to set up your software environment
• About the technical capabilities of modern day CPUs and GPUs
• How to identify bottlenecks in your code

Who?

Everyone interested in getting familiar with machine learning at scale, from the beginning up to more advanced topics

Prerequisites

• Basic knowledge on statistics
• Basic knowledge on linear algebra
• Basic knowledge on Python programming. Some experience with the use of Jupyter Notebooks is desirable, but not essential.

* Basic knowledge on parallel computing is helpful, but not required.