The Little Book of Deep Learning by François Fleuret

Although the bulk of deep learning is not difficult to understand, it combines diverse components such as linear algebra, calculus, probabilities, optimization, signal processing, programming, algorithmics, and high-performance computing, making it complicated to learn.

Instead of trying to be exhaustive, this little book is limited to the background necessary to understand a few important models. This proved to be a popular approach, resulting in more than 500,000 downloads of the PDF file in the 12 months following its announcement on Twitter.

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