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General Information

The eScience Center offers a range of free workshops and training courses, open to all researchers affiliated with Dutch research organizations. We organize workshops covering digital skills needed to put reproducible research into practice. These include online collaboration, reproducible code and good programming practices. We also offer more advanced workshops such as GPU Programming, Parallel Programming and Deep Learning.

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.

Where: Science Park 402, 1098 XH Amsterdam. Get directions with OpenStreetMap or Google Maps.

When: September 26 - 27, 2022, 9:30 - 16:30 CEST.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).

Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:

• The room is wheelchair / scooter accessible.
• Accessible restrooms are available.

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.

Contact: Please email or training@esciencecenter.nl for more information.

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Code of Conduct

Participants are expected to follow those guidelines:

• Use welcoming and inclusive language
• Be respectful of different viewpoints and experiences
• Gracefully accept constructive criticism
• Focus on what is best for the community
• Show courtesy and respect towards other community members

Syllabus

Introduction

• Recall the sort of problems for which Deep Learning is a useful tool
• List some of the available tools for Deep Learning
• Recall the steps of a Deep Learning workflow
• Explain why it is important to test the accuracy of Deep Learning system.
• Identify the inputs and outputs of a Deep Learning system.
• Test that we’ve correctly installed the Keras, Seaborn and Sklearn libraries

Classification by a Neural Network using Keras

• Use the deep learning workflow to structure the notebook
• Explore the dataset using pandas and seaborn
• Use one-hot-encoding to prepare data for classification in Keras
• Describe a fully connected layer
• Implement a fully connected layer with Keras
• Use Keras to train a small fully connected network on prepared data
• Plot the loss curve of the training process
• Use a confusion matrix to measure the trained networks’ performance on a test set

Monitor the training process

• Explain the importance of splitting the training data
• Use the data splits to plot the training process
• Set the training goal for your deep neural network
• Measure the performance of your deep neural network
• Interpret the training plots to recognize overfitting
• Implement basic strategies to prevent overfitting

Networks are like onions

• Understand why convolutional and pooling layers are useful for image data
• Use normalization as preparation step for Deep Learning
• Implement a convolutional neural network on an image dataset
• Plot and interpret the training process
• Do a visual inspection of the results of your neural network
• Understand strategies to improve your model based on the plots

Schedule

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Setup

To participate in this workshop, you will need access to software as described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

Software setup

Please follow these setup instructions in preparation for the workshop.