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

Related tags

Deep Learningcourseresearchcomputer-visiondeep-learningpytorchanki
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
Owner
Arthur Douillard
PhD Student @ Sorbonne, Research Scientist @ Heuritech, Lecturer @ EPITA
Arthur Douillard
GitHub Repository https://arthurdouillard.com/deepcourse/
CLADE - Efficient Semantic Image Synthesis via Class-Adaptive Normalization (TPAMI 2021)

Efficient Semantic Image Synthesis via Class-Adaptive Normalization (Accepted by TPAMI)

tzt 49 Nov 17, 2022
Testing and Estimation of structural breaks in Stata

xtbreak estimating and testing for many known and unknown structural breaks in time series and panel data. For an overview of xtbreak test see xtbreak

Jan Ditzen 13 Jun 19, 2022
Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting

Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting #Dataset The folder "Dataset" contains the dataset use in this work and m

0 Jan 08, 2022
Sequential Model-based Algorithm Configuration

SMAC v3 Project Copyright (C) 2016-2018 AutoML Group Attention: This package is a reimplementation of the original SMAC tool (see reference below). Ho

AutoML-Freiburg-Hannover 778 Jan 05, 2023
Code used to generate the results appearing in "Train longer, generalize better: closing the generalization gap in large batch training of neural networks"

Train longer, generalize better - Big batch training This is a code repository used to generate the results appearing in "Train longer, generalize bet

Elad Hoffer 145 Sep 16, 2022
Non-stationary GP package written from scratch in PyTorch

NSGP-Torch Examples gpytorch model with skgpytorch # Import packages import torch from regdata import NonStat2D from gpytorch.kernels import RBFKernel

Zeel B Patel 1 Mar 06, 2022
Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX.

Python scripts for performing object detection with the 1000 labels of the ImageNet dataset in ONNX. The repository combines a class agnostic object localizer to first detect the objects in the image

Ibai Gorordo 24 Nov 14, 2022
Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

Recursive-NeRF: An Efficient and Dynamically Growing NeRF This is a Jittor implementation of Recursive-NeRF: An Efficient and Dynamically Growing NeRF

33 Nov 30, 2022
Preprocessed Datasets for our Multimodal NER paper

Unified Multimodal Transformer (UMT) for Multimodal Named Entity Recognition (MNER) Two MNER Datasets and Codes for our ACL'2020 paper: Improving Mult

76 Dec 21, 2022
Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.

Equinox Callable PyTrees and filtered JIT/grad transformations = neural networks in JAX Equinox brings more power to your model building in JAX. Repr

Patrick Kidger 909 Dec 30, 2022
A minimal implementation of Gaussian process regression in PyTorch

pytorch-minimal-gaussian-process In search of truth, simplicity is needed. There exist heavy-weighted libraries, but as you know, we need to go bare b

Sangwoong Yoon 38 Nov 25, 2022
Histocartography is a framework bringing together AI and Digital Pathology

Documentation | Paper Welcome to the histocartography repository! histocartography is a python-based library designed to facilitate the development of

155 Nov 23, 2022
ISBI 2022: Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image.

Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Introduction This repository contains the PyTorch implem

25 Nov 09, 2022
Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".

nvdiffrec Joint optimization of topology, materials and lighting from multi-view image observations as described in the paper Extracting Triangular 3D

NVIDIA Research Projects 1.4k Jan 01, 2023
This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning It includes /bert, which is the original BERT repos

Mitchell Gordon 11 Nov 15, 2022
Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

Residual Dense Network for Image Super-Resolution This repository is for RDN introduced in the following paper Yulun Zhang, Yapeng Tian, Yu Kong, Bine

Yulun Zhang 494 Dec 30, 2022
wgan, wgan2(improved, gp), infogan, and dcgan implementation in lasagne, keras, pytorch

Generative Adversarial Notebooks Collection of my Generative Adversarial Network implementations Most codes are for python3, most notebooks works on C

tjwei 1.5k Dec 16, 2022
Differentiable molecular simulation of proteins with a coarse-grained potential

Differentiable molecular simulation of proteins with a coarse-grained potential This repository contains the learned potential, simulation scripts and

UCL Bioinformatics Group 44 Dec 10, 2022
3D Human Pose Machines with Self-supervised Learning

3D Human Pose Machines with Self-supervised Learning Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, and Pengxu Wei, “3D Human Pose Machines with Self

Chenhan Jiang 398 Dec 20, 2022
Cosine Annealing With Warmup

CosineAnnealingWithWarmup Formulation The learning rate is annealed using a cosine schedule over the course of learning of n_total total steps with an

zhuyun 4 Apr 18, 2022