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- Part 1 - Introduction
- 1.1: Introduction: machine learning, a couple of applications
[36 slides]
- 1.2: A short history of deep learning
[8 slides]
- 1.3: An extremely short crash course on fitting and generalization
[16 slides]
- Part 2 - Neural Networks and PyTorch
- Part 3 - Scaling up: computer vision, transfer learning, visualization
- Part 4 - Structure: sequences, graphs, attention
- Part 5 - Advanced applications
- Part 6 - A gentle introduction into learning theory
- 5.1: A gentle introduction into learning theory
[45 slides]
- 5.2: A concrete application of the usage of learning theory in DL
[35 slides]
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PyTorch sources:
The full PyTorch sources of the slides are here.
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Pratical Excercise session:
(will be held by Edward Beeching, web site will be published shortly.)
Neural models in python for object detection and
future forecasting.
[Project page]
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