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Apply for the Keep-Current Applied Deep Learning Course using fastai and PyTorch
Welcome to the Applied Deep Learning course, based on the “Practical Deep Learning for Coders” (v3) fast.ai MOOC with PyTorch 1.0.

You will learn the practical details of deep learning applications with hands-on coding using the fastai library built on top of PyTorch.

The key deep learning applications covered in the MOOC are:
> Computer vision
> Natural language processing
> Tabular data
> Collaborative filtering

You are expected to have at least 1 year experience with programming. Preferably, you should feel comfortable with programming in Python as well as having basic knowledge in Calculus and Linear Algebra. Some machine learning background is advised to make best use of the course, but is not necessary.

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Course Structure
The course is made of 7 meetings. Before each meeting, you are expected to watch the course video and arrive to the meeting with insights and questions. The questions will be answered by other members in the study group. Thus, enabling members to learn and understand better by teaching others. As we all come from different backgrounds, we will enrich each other by expressing and emphasizing the different aspects of deep learning algorithms in practice and theory.

The first premeeting will be held in the middle of February. The exact dates for the 7 lectures meetings will be agreed on at the first premeeting.
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