Introduction to Deep Learning

This workshop will be delivered in person, unless new COVID-19 restrictions are put in place. The workshop will take place at Science Park 402, 1098 XH Amsterdam. Lunch and drinks at the end of the workshop are included.

This is an 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 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.

The workshop is based on the teaching style of the Carpentries, and learners will follow along while the instructors write the code on screen. More information can be found on the workshop website.

Who: The workshop is open and free to all researchers in the Netherlands at PhD candidate level and higher. We do not accept registrations by Master students. The workshop is aimed at PhD candidates and other researchers or research software engineers.

Prerequired knowledge: Learners are expected to have the following knowledge:

• Basic Python programming skills and familiarity with the Pandas package
• Basic knowledge on Machine learning, including the following concepts: Data cleaning, train & test split, type of problems (regression, classification), overfitting & underfitting, metrics (accuracy, recall, etc.).

Syllabus

• Introduction to Deep Learning concepts
• Steps of the Deep Learning workflow
• Training a model for classification
• Monitoring the training process
• Avoiding overfitting
• Convolutional and Pooling layers