Deep Learning

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

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Adapted from Emmanuel Rachelson's Machine Learning class

Syllabus

This class covers deep learning from a theoretical basis to example applications. 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.

2024 Schedule

Schedule
12/11	Artificial Neural Networks	ANNs, backpropagation, Stochastic Gradient Descent
12/11	Deep Learning	layers, convolution, architectures, training
26/11	Deep Learning for Computer Vision, pt 1	Convolutional Neural Networks, satellite imagery
26/11	Deep Learning for Computer Vision, pt 2
03/12	Image generation	VAEs, GANs, and Diffusion Models
09/12	RNNs	Recurrent Neural Networks, LSTM, GRU
10/12	Transformers	Transformers
17/12	Dimensionality Reduction	Autoencoders, t-SNE
17/12	LLMs	Large Language Models