fchouteau/deep-learning
Deep Learning section of the Algorithms in Machine Learning class at ISAE-Supaero
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 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.
2020 Schedule
| Schedule | ||
|---|---|---|
| 17/11 | Artificial Neural Networks | ANNs, backpropagation, Stochastic Gradient Descent |
| 23/11 | Deep Learning | layers, convolution, architectures, training |
| 30/11 | Deep Learning for Computer Vision, pt 1 | Convolutional Neural Networks, satellite imagery |
| 01/12 | Deep Learning for Computer Vision, pt 2 | |
| 07/12 | RNNs | Recurrent Neural Networks, LSTM, GRU |
| 08/12 | NLP | Natural Language Processing, Transformers |
| 15/12 | GANs | Generative Adversarial Networks, CycleGAN |
| 16/12 | Dimensionality Reduction | Autoencoders, t-SNE |