Deep Learning section of the Algorithms in Machine Learning class at ISAE-Supaero

• Home
• Github repository

Adapted from Emmanuel Rachelson's Machine Learning class

This class covers deep learning in a total of 24 hours over 5 weeks. We start with simple multi-layer perceptrons, backpropogation, and gradient descent, exploring at the fundamental aspects of deep learning in depth. We cover a wide range of deep learning topics, from Natural Language Processing to Generative Adversarial Networks; the full schedule is below. The goal is that students understand the capacities of deep learning, the current state of the field, and the challenges of using and developing deep learning algorithms. By the end of this class, we expect students that students will be able to understand recent literature in deep learning, implement novel neural network architectures, use and understand the PyTorch library in many ways, and apply deep learning to different domains.