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Deep Learning Course
This document provides resources for François Fleuret's deep-learning course at the University of Geneva. The course offers a thorough introduction to deep learning, with examples using the PyTorch framework. The materials include slides, recordings, and a virtual machine. The course covers topics such as machine learning objectives, tensor operations, automatic differentiation, gradient descent, and deep-learning techniques. The document also includes prerequisites for the course, such as knowledge of linear algebra, differential calculus, Python programming, and probability and statistics.
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