Deep Learning🔗
Deep Learning section of the Algorithms in Machine Learning class at ISAE-Supaero
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.
2026-2027 Schedule🔗
| Schedule | ||
|---|---|---|
| 13/10 | Artificial Neural Networks | ANNs, backpropagation, Stochastic Gradient Descent |
| 13/10 | Deep Learning | layers, convolution, architectures, training |
| 03/11 | Deep Learning for Computer Vision, pt 1 | Convolutional Neural Networks |
| 03/11 | Deep Learning for Computer Vision, pt 2 | CNNs and satellite imagery |
| 10/11 | Autoencoders and Self-Supervised Learning | autoencoders, SSL |
| 10/11 | Dimensionality Reduction | t-SNE, SAEs |
| 17/11 | Image generation, pt 1 | VAEs and GANs |
| 17/11 | Image generation, pt 2 | Diffusion Models |
| 24/11 | RNNs | Recurrent Neural Networks, LSTM, GRU |
| 24/11 | Deep Learning in Practice | training, debugging, and deploying deep models |
| 30/11 | Transformers | the Transformer architecture |
| 01/12 | LLMs | Large Language Models |
| 01/12 | Retrieval-Augmented Generation | RAG |