Learn the Deep Learning for Computer Vision in three steps: theory from base to SotA, code in PyTorch, and space-repetition with Anki

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Deep Learning course research computer-vision deep-learning pytorch anki
Overview

DeepCourse: Deep Learning for Computer Vision

arthurdouillard.com/deepcourse/

Picture of the website front page with the skill tree

This is a course I'm giving to the French engineering school EPITA each Fall. This course is the new version (as of 2021) of my lectures serie. It's given in around 24h, with sessions of 4 hours each made of 2 hours of lessons and 2 hours of coding.

If you find a mistake (in the lesson, notebook, or quiz), please open an issue here. Likewise, if you have ideas to improve this course (should I cover topic X? etc.), please also open an issue.

Roadmap to do

  • Add all major lessons + notebooks + quiz
  • New architectures
    • Lesson (transformer, new kind of mlp, involution, NAS, what else?)
    • Notebook transformer
    • Notebook MLP-Mixer
    • Quiz
  • Robustness (uncertainty, adversarial attack)
    • Lesson
    • Notebook
    • Quiz
  • Continual Learning + domain generalization + out-of-distribution
    • Lesson
    • Notebooks
    • Quiz
  • Graph Neural Network
  • Lesson
  • Notebooks
  • Quiz
  • Make website look nice on every kind of devices & screens
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Comments
  • minor fixes in MLP backprop

    minor fixes in MLP backprop

    Hi Arthur, nice work on Deep Course! I was going through it to have a refresher on CV and noticed some potential errors on mlp.py:

    1. the softmax in self.fit is redundant since it was already performed in self.forward to produce the probs
    2. grad_bh depends on grad_htilde, not grad_h, since tanh is performed on XW+b
    opened by navivokaj 1
Owner
Arthur Douillard
PhD Student @ Sorbonne, Research Scientist @ Heuritech, Lecturer @ EPITA
Arthur Douillard
GitHub https://arthurdouillard.com/deepcourse/
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