- ImageNet Classification with Deep Convolutional Neural Networks [2012] 💥
- Fully Convolutional Networks for Semantic Segmentation [2014]
- Generative Adversarial Networks [2014]
- Representation Learning: A Review and New Perspectives [2014]
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition [2014]
- Siamese Neural Networks for One-shot Image Recognition [2015]
- U-Net: Convolutional Networks for Biomedical Image Segmentation [2015]
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [2015]
- Is object localization for free? – Weakly-supervised learning with convolutional neural networks [2015] ✏️
- Striving for Simplicity: The All Convolutional Net [2015]
- From Image-level to Pixel-level Labeling with Convolutional Networks [2015] ✏️
- A guide to convolution arithmetic for deep learning [2016]
- CAM [2016] ✏️
- 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation [2016]
- NIPS 2016 Tutorial: Generative Adversarial Networks [2016]
- Densely Connected Convolutional Networks [2017]
- Grad-CAM [2017] ✏️
- On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima [2017]
- Don't Decay the Learning Rate, Increase the Batch Size [2017]
- Stronger generalization bounds for deep nets via a compression approach [2018]
- Group Normalization [2018]
- Few-Shot Adversarial Learning of Realistic Neural Talking Head Models [2019]
- DCU-Net [2019]
- Deep Learning - Ian Goodfellow and Yoshua Bengio book
- Deep learning with PyTorch
- Interpretable Machine Learning
- Reinforcement Learning: An Introduction
- Algorithms for Reinforcement Learning
- Mathematics for Machine Learning - Marc Deisenroth
- Mathematics for machine Learning - Garrett Thomas
- The Matrix Calculus You Need For Deep Learning
- VIP AI 101 CHEATSHEET