### State-of-the-art Deep Learning publications, frameworks & resources

#### Overview

Deep convolutional neural networks have led to a series of breakthroughs in large-scale image and video recognition. This repository aims at presenting an elaborate list of the latest state-of-the-art works on the field of Deep Learning since 2013.

*This is going to be an evolving post and I will keep updating it (at least twice monthly) so make sure you have forked this repository on GitHub before moving on ! *

#### State-of-the-art papers (Descending order based on Google Scholar Citations)

- Very deep convolutional networks for large-scale image recognition (VGG-net) (2014) [pdf] [video]
- Going deeper with convolutions (GoogLeNet) by Google (2015) [pdf] [video]
- Deep learning (2015) [pdf]
- Visualizing and Understanding Convolutional Neural Networks (ZF Net) (2014) [pdf] [video]
- Fully convolutional networks for semantic segmentation (2015) [pdf]
- Deep residual learning for image recognition (ResNet) by Microsoft (2015) [pdf] [video]
- Deepface: closing the gap to human-level performance in face verification (2014) [pdf] [video]
- Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015) [pdf]
- Deep Learning in Neural Networks: An Overview (2015) [pdf]
- Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (PReLU) (2014) [pdf]
- Faster R-CNN: Towards real-time object detection with region proposal networks (2015) [pdf]
- Fast R-CNN (2015) [pdf]
- Spatial pyramid pooling in deep convolutional networks for visual recognition (SPP Net) (2014) [pdf] [video]
- Generative Adversarial Nets (2014) [pdf]
- Spatial Transformer Networks (2015) [pdf] [video]
- Understanding deep image representations by inverting them (2015) [pdf]
- Deep Learning of Representations: Looking Forward (2013) [pdf]

### Classic publications

- ImageNet Classification with Deep Convolutional Neural Networks (AlexNet) (2012) [pdf]
- Rectified linear units improve restricted boltzmann machines (ReLU) (2010) [pdf]

### Theory

- Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images (2015) [pdf]
- Distilling the Knowledge in a Neural Network (2015) [pdf]
- Deep learning in neural networks: An overview (2015) [pdf]

### Books

- Deep Learning Textbook - An MIT Press book (2016) [html]
- Learning Deep Architectures for AI [pdf]
- Neural Nets and Deep Learning [html] [github]

### Courses / Tutorials (Webpages unless other is stated)

- Caffe Tutorial (CVPR 2015)
- Tutorial on Deep Learning for Vision (CVPR 2014)
- Introduction to Deep Learning with Python - Theano Tutorials [github]
- Deep Learning Tutorials with Theano/Python [github]
- Deep Learning: Take machine learning to the next level (by udacity)
- DeepLearnToolbox – A Matlab toolbox for Deep Learning [github]
- Stanford Matlab-based Deep Learning [github]
- Stanford 231n Class: Convolutional Neural Networks for Visual Recognition [github]
- Deep Learning Course (by Yann LeCun-2016)
- Generative Models (by OpenAI)
- An introduction to Generative Adversarial Networks (with code in TensorFlow)

### Resources / Models (GitHub repositories unless other is stated)

- VGG-net
- GoogLeNet
- ResNet - MatConvNet implementation
- AlexNet
- Fully Convolutional Networks for Semantic Segmentation
- OverFeat
- SPP_net
- Fast R-CNN
- Faster R-CNN
- Generative Adversarial Networks (GANs)
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks)

### Frameworks & Libraries (Descending order based on GitHub stars)

- Tensorflow by Google [C++ and CUDA]: [homepage] [github]
- Caffe by Berkeley Vision and Learning Center (BVLC) [C++]: [homepage] [github] [Installation Instructions]
- Keras by François Chollet [Python]: [homepage] [github]
- Microsoft Cognitive Toolkit - CNTK [C++]: [homepage] [github]
- MXNet adapted by Amazon [C++]: [homepage] [github]
- Torch by Collobert, Kavukcuoglu & Clement Farabet, widely used by Facebook [Lua]: [homepage] [github]
- Convnetjs by Andrej Karpathy [JavaScript]: [homepage] [github]
- Theano by Université de Montréal [Python]: [homepage] [github]
- Deeplearning4j by startup Skymind [Java]: [homepage] [github]
- Paddle by Baidu [C++]: [homepage] [github]
- Deep Scalable Sparse Tensor Network Engine (DSSTNE) by Amazon [C++]: [github]
- Neon by Nervana Systems [Python & Sass]: [homepage] [github]
- Chainer [Python]: [homepage] [github]
- h2o [Java]: [homepage] [github]
- Brainstorm by Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA) [Python]: [github]
- Matconvnet by Andrea Vedaldi [Matlab]: [homepage] [github]

##### (Last updated 15.2.2017)

**Do you know someone who would like this?**

**Click Share !**