Introduction to Deep Learning

This is a hands-on introduction to the first steps in deep learning, intended for researchers who are familiar with (non-deep) machine learning.

The use of deep learning has seen a sharp increase of popularity and applicability over the last decade. While deep learning can be a useful tool for researchers from a wide range of domains, taking the first steps in the world of deep learning can be somewhat intimidating. This introduction covers the basics of deep learning in a practical and hands-on manner, so that upon completion, you will be able to train your first neural network and understand what next steps to take to improve the model.

We start with explaining the basic concepts of neural networks, and then go through the different steps of a deep learning workflow. Learners will learn how to prepare data for deep learning, how to implement a basic deep learning model in Python with Keras, how to monitor and troubleshoot the training process and how to implement different layer types such as convolutional layers.

Preparation

Schedule

Day 1

Time

Topic

9:00

Welcome and Icebreaker

9:10

Introduction to Deep Learning

9:50

Coffee Break

10:00

Classification by a neural network using Keras

10:50

Coffee Break

11:00

Classification by a neural network using Keras

11:50

Wrap-up

12:00

END

Day 2

Time

Topic

9:00

Welcome and recap

9:10

Monitor the training process

9:50

Coffee Break

10:00

Monitor the training process

10:20

Advanced Layer Types

10:50

Coffee Break

11:00

Advanced Layer Types

11:20

Transfer learning

11:40

Outlook

11:50

Wrap-up

12:00

END

Day 3

Time

Topic

9:00

Welcome and recap

9:05

TBD

9:55

Coffee Break

10:00

TBD

10:50

Coffee Break

11:00

TBD

11:50

Post-workshop survey

12:00

END