1.Architectures
AlexNet: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks ZFNet: https://arxiv.org/abs/1311.2901 VGG16: https://arxiv.org/abs/1505.06798 ResNet: https://arxiv.org/abs/1704.06904 GoogLeNet: https://arxiv.org/abs/1409.4842 Inception: https://arxiv.org/abs/1512.00567 Xception: https://arxiv.org/abs/1610.02357 MobileNet: https://arxiv.org/abs/1704.04861
Semantic Segmentation
FCN: https://arxiv.org/abs/1411.4038 SegNet: https://arxiv.org/abs/1511.00561 UNet: https://arxiv.org/abs/1505.04597 PSPNet: https://arxiv.org/abs/1612.01105 DeepLab: https://arxiv.org/abs/1606.00915 ICNet: https://arxiv.org/abs/1704.08545 ENet: https://arxiv.org/abs/1606.02147
Generative adversarial networks
GAN: https://arxiv.org/abs/1406.2661 DCGAN: https://arxiv.org/abs/1511.06434 WGAN: https://arxiv.org/abs/1701.07875 Pix2Pix: https://arxiv.org/abs/1611.07004 CycleGAN: https://arxiv.org/abs/1703.10593
Object detection
RCNN: https://arxiv.org/abs/1311.2524 Fast-RCNN: https://arxiv.org/abs/1504.08083 Faster-RCNN: https://arxiv.org/abs/1506.01497 SSD: https://arxiv.org/abs/1512.02325 YOLO: https://arxiv.org/abs/1506.02640 YOLO9000: https://arxiv.org/abs/1612.08242
2.https://www.youtube.com/watch?v=RtqtM1UJfZc //feature_engg_tips
3.https://www.youtube.com/watch?v=ZfxNcO6BqDo //understanding VAE
4.https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc // deep reinforcement learning course 2020
5.https://github.com/ryancheunggit/Denoise-Transformer-AutoEncoder
6.https://www.youtube.com/playlist?list=PLog3nOPCjKBnjhuHMIXu4ISE4Z4f2jm39 //advanced computer vision course 2020
8.https://amaarora.github.io/ // very good blogs
9.https://karpathy.github.io/ // very good blogs
10.https://kozodoi.me/blog/ // good set of blogs
11.https://sgugger.github.io/how-do-you-find-a-good-learning-rate.html //finding good Lr
12.https://neptune.ai/blog/graph-neural-network-and-some-of-gnn-applications
14.https://arxiv.org/pdf/2106.11959.pdf //deep learning on tabular data, you can also check 'Tabnet'
15.https://github.com/fastai/fastbook the whole official fastbook, very good for beginners in deep learning
16.https://fullstackdeeplearning.com/spring2021/lecture-7/ Troubleshooting Deep Neural Networks , Very important
17.https://sebastianraschka.com/blog/2021/dl-course.html another great deep learning course
18.https://efficientdl.com/faster-deep-learning-in-pytorch-a-guide/ Faster Deep Learning Training with PyTorch
19.https://www.analyticsvidhya.com/blog/2021/06/how-to-load-kaggle-datasets-directly-into-google-colab/ loading kaggle datasets to colab
20.https://github.com/jbhuang0604/awesome-tips awesome tips regarding general research (Not only ML)
21.https://www.youtube.com/watch?v=9mS1fIYj1So PyTorch Performance Tuning Guide - Szymon Migacz, NVIDIA
22.https://huyenchip.com/ml-interviews-book/ for your ML iterviews!
23.https://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/ frequentism vs bayesianism
24.https://davidrosenberg.github.io/ml2016/#home MACHINE LEARNING AND COMPUTATIONAL STATISTICS course by legend David Rosenberg
25.https://cds.nyu.edu/deep-learning/ Deep Learning course(my favourite course) by OG Yann LeCun(spring 2021 edition)
26.https://nn.labml.ai/ Annotated PyTorch Paper Implementations
27.https://ikhlestov.github.io/pages/machine-learning/convolutions-types/ Different types of the convolution layers
28.https://amitness.com/ nice set of blogs
29.https://medium.com/@hirotoschwert/digging-into-detectron-2-47b2e794fabd FasterRcnn in detecron2
30.https://medium.com/@chris.p.hughes10 nice set of blogs
31.https://explained.ai/matrix-calculus/ Matrix Calculus
32.https://myrtle.ai/how-to-train-your-resnet-8-bag-of-tricks/ Training tricks for Resnet(can be applied to other models too)