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This is a 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.
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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.).
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::: instructor
Do you want to teach this lesson? Find more help in the README Feel free to reach out to us with any questions that you have. Just open a new issue. We also value any feedback on the lesson!
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::: instructor
Episode 2, 3, and 4 in this lesson are relatively long. We suggest to have a break at least every 90 minutes and to switch the instructor regularly, also within episodes. We have added reminders to the longer episodes with suggestions for when to have a switch and/or a break.
There is an example schedule with breaks that can be adapted to how you want to teach the lesson.
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