README.md

Preliminaries

Git tutorial

git 게임 git tutorial for dummies

Docker tutorial

docker for machine learning

ssh tutorial

ssh for dummies

python tutorial

virtual environment conda environment

cuda tutorial

yt

Recommended Papers

Core Machine Learning

• Lottery Ticket Hypothesis
  https://arxiv.org/abs/1803.03635

• Double Descent
  https://arxiv.org/abs/1912.02292

Model Architectures

• ResNet
  https://arxiv.org/abs/1512.03385

• Transformer
  https://arxiv.org/abs/1706.03762

• Vision Transformer
  https://arxiv.org/abs/2010.11929

(2D) Object Detection

• Faster R-CNN
  https://arxiv.org/abs/1506.01497

• SSD
  https://arxiv.org/abs/1512.02325

• YOLO
  https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf

(3D) Object Detection

• PointNet
  https://arxiv.org/abs/1612.00593

• PointNet++
  https://arxiv.org/abs/1706.02413

• VoxelNet
  https://arxiv.org/abs/1711.06396

• PointRCNN
  https://arxiv.org/abs/1812.04244

• PV-RCNN
  https://arxiv.org/abs/1912.13192

Unsupervised Domain Adaptation

• Domain-Adversarial Training of Neural Networks
  https://arxiv.org/pdf/1505.07818

• Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
  https://arxiv.org/abs/1906.03950

• Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
  https://arxiv.org/abs/1712.02560

• Domain Adaptive Faster R-CNN for Object Detection in the Wild
  https://arxiv.org/abs/1803.03243

Self-supervised Learning

• MoCo
  https://arxiv.org/abs/1911.05722

• SimCLR
  https://arxiv.org/abs/2002.05709

• BYOL
  https://arxiv.org/abs/2006.07733

Implicit Neural Representations

• SDF
  https://arxiv.org/abs/1901.05103

• NeRF
  https://arxiv.org/abs/2003.08934

Generative Models

• VAE
  https://arxiv.org/abs/1312.6114

• VQ-VAE
  https://arxiv.org/abs/1711.00937

• DCGAN
  https://arxiv.org/abs/1511.06434

• StyleGAN
  https://arxiv.org/abs/1812.04948

• Denoising Diffusion Probabilistic Models
  https://arxiv.org/abs/2006.11239

• GLIDE
  https://arxiv.org/abs/2112.10741

Adversarial Attack/Defense

• FGSM
  https://arxiv.org/abs/1412.6572

• PGD
  https://arxiv.org/pdf/1706.06083.pdf

• Adversarial Examples Are Not Bugs, They Are Features
  https://arxiv.org/abs/1905.02175

Vision-Language

• CLIP
  https://arxiv.org/abs/2103.00020

• BLIP
  https://arxiv.org/abs/2201.12086

• SimVLM
  https://openreview.net/forum?id=GUrhfTuf_3

• CoCa
  https://arxiv.org/abs/2205.01917

• Dall-E
  https://arxiv.org/abs/2102.12092

• Dall-E-2
  https://arxiv.org/abs/2204.06125

Natural Language

• BERT
  https://arxiv.org/abs/1810.04805

• BART
  https://arxiv.org/abs/1910.13461

• GPT
  https://arxiv.org/abs/2005.14165

Courses & Lectures

Linear Algebra

• Gilbert Strang (MIT)
  Course Link

Optimization

• Stephen Boyd (Stanford)
  Course Link

Deep Learning

• Soheil Feizi (University of Maryland, College Park)
  Course Link

• Alexander Amini (MIT)
  Course Link

Computer Vision

• CS231n (Stanford)
  Course Link

Computer Graphics

• Keenan Crane (CMU)
  Course Link

Textbooks

Deep Learning

• Ian Goodfellow, Yoshua Bengio, Aaron Courville
  Textbook Link

Pattern Recognition and Machine Learning

• Christopher Bishop
  Textbook Link