Introduction to Deep-Learning

Overview

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 aims to cover 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.

After attending the workshop, you should be able to:

• Prepare input data for use for deep learning
• Design and train a Deep Neural Network
• Troubleshoot the learning process
• Measure the performance of the network
• Visualise data and results
• Re-use existing network architectures with and without pre-trained weights

Prerequisites

Participants are expected to have basic Python programming skills and familiarity with the Pandas package. Basic knowledge of statistics is also beneficial.

Preliminary Agenda

Tuesday 22 February 2022

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Wednesday 23 February 2022

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Registration

Please register by following this link https://events.prace-ri.eu/event/1295/registrations/955/

For questions regarding this event please contact us at training@enccs.se.

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This training is intended for users established in the European Union or a country associated to Horizon 2020.