Introduction to Deep Learning

The workshop will take place at Science Park 402, 1098 XH Amsterdam. Please note that lunch and drinks at the end of the workshop are included.

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 by 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.

Cancellation and no-show policy

Please be advised that by signing up, you agree to our Cancellation and No-Show Policy, which states that cancellations made less than 2 working days prior to the event will incur a no-show fee. Please read the full policy here for more details.

If you won’t be able to attend, please cancel your registration (by following the instructions here) so that we can offer your seat to someone on the waiting list.

Audience

The workshop is aimed at PhD candidates, postdocs, (assistant-)professors, and other researchers and is also open for research support staff and research software engineers. We offer tickets for researchers who are affiliated with Dutch research institutions. We also offer industry tickets for attendees who are not affiliated with Dutch research institutions. We do not accept registrations by Master students.

Ticket prices

Ticket prices are as follows:

• For participants affiliated with Dutch research institutions: €225
• For participants from industry: €675

Early Bird Discount

Register within the first week of open registration and receive a 25% discount on your participation fee. Don’t miss out on this opportunity to secure your spot at a reduced price!

Group Discount

Bring your team along and benefit from our group discount. Groups of five or more participants are eligible for a 25% discount. For more details and to register your group, please email us at dHJhaW5pbmcgfCBlc2NpZW5jZWNlbnRlciAhIG5s.

Prerequisite 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.).

Please take this , if you answered "No" to most of the questions, this workshop may be too advanced for you, and you may want to consider signing up in the future when you have the prerequired knowledge. You could consider following a data carpentry workshop to learn about data preprocessing and/or learn more about machine learning in this course. Check our upcoming workshops here. Please contact dHJhaW5pbmcgfCBlc2NpZW5jZWNlbnRlciAhIG5s if you have any questions about the prerequisites or course content.

Note that this workshop is an introduction into deep learning. If you are already familiar with the concepts in the syllabus then a more advanced course might be better suited for your interests!

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 organizing a deep learning project”
• “Identify next steps to take after this course”

Contact

If you have any questions, please send an email to dHJhaW5pbmcgfCBlc2NpZW5jZWNlbnRlciAhIG5s.

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