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General Information

The eScience Center offers a range of workshops and training courses, aimed at PhD candidates and other researchers or research software engineers. 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, Image Processing and Deep Learning.

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

The use of deep learning has seen a sharp increase in 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 03 - 04, 2024, 9:30 - 17:00 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.

Workshop files: You will find all slides, notebooks, archived collaborative documents, and other relevant files in the files folder of the workshop website repository after the workshop.

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

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Code of Conduct

Participants are expected to follow these 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”
• “Identify the inputs and outputs of a deep neural network.”
• “Explain the operations performed in a single neuron”
• “Test that you have correctly installed the Keras, Seaborn and Sklearn libraries”
• “Describe what a loss function is”

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”
• “Interpret 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 keeping your test set clean, by validating on the validation set instead of the test set”
• “Use the data splits to plot the training process”
• “Explain how optimization works”
• “Design a neural network for a regression task”
• “Measure the performance of your deep neural network”
• “Interpret the training plots to recognize overfitting”
• “Use normalization as preparation step for deep learning”
• “Implement basic strategies to prevent overfitting”

Advanced layer types

• “Understand why convolutional and pooling layers are useful for image data”
• “Implement a convolutional neural network on an image dataset”
• “Use a drop-out layer to prevent overfitting”

Transfer learning:

• “Adapt a state-of-the-art pre-trained network to your own dataset”

Outlook

• “Understand that what we learned in this course can be applied to real-world problems”
• “Use best practices for organising a deep learning project”
• “Identify next steps to take after this course”

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.

Make sure to download the required datasets as well:

• Weather dataset
• Dollar street dataset (4 files in total)