Cutting-edge deep learning papers, architecture and resource sharing

Deep convolutional neural networks have been a great success in the field of image, speech, and NLP, and from the perspective of learning and sharing, this article has compiled the latest resources on CNN related since 2013, including important papers, books, video tutorials, Tutorial, theories, model libraries, and development libraries. At the end of the text is attached to the resource address.

Important Papers:

1. Very deep convolutional networks for large-scale image recognition (vgg-net) (2014)

2. Going deeper with convolutions (googlenet) by Google (2015)

3. Deep Learning (2015)

4. Visualizing and understanding convolutional neural Networks (ZF Net) (2014)

5. Fully convolutional Networks for Semantic segmentation (2015)

6. Deep residual learning for image recognition (ResNet) by Microsoft (2015)

7. DeepFace closing the gap to Human-level performance on face verification (2014)

8. Batch Normalization accelerating deep network training by reducing internal covariate shift (2015)

9. Deep learning in neural Networks an overview (2015)

Delving deep to rectifiers surpassing Human-level performance on Imagenet classification (Prelu) (2014)

Faster r-cnn Towards Real-time object detection with region proposal Networks (2015)

Fast r-cnn (2015)

Spatial pyramid Pooling in deep convolutional networks for visual recognition (SPP Net) (2014)

Generative adversarial Nets (2014)

Spatial Transformer Networks (2015)

Understanding deep image representations by inverting them (2015)

. Deep learning of representations looking Forward (2013)

The classic article:

Magenet classification with deep convolutional neural Networks (AlexNet) (2012)

Rectified linear units improve restricted Boltzmann machines (ReLU) (2010)

Important Theory:

Deep neural Networks is easily fooled high Confidence predictions for unrecognizable Images (2015)

Distilling the knowledge in a neural Network (2015)

Deep learning in neural networks A overview (2015)

Important Books:

Learning Textbook-an MIT Press book (2016)

Learning deep architectures for AI

Neural Nets and deep learning

Important Course/tutorial:

Caffe Tutorial (CVPR 2015)

Tutorial on Deep Learning for Vision (CVPR 2014)

Introduction to deep learning with Python-theano tutorials

Deep Learning Tutorials with Theano/python

Learning take machine learning to the next level (by Udacity)

Deeplearntoolbox–a Matlab Toolbox for deep learning

Stanford matlab-based Deep Learning

Stanford 231n Class convolutional neural Networks for Visual recognition

Learning Course (by Yann LeCun-2016)

Generative Models (by OpenAI)

Introduction to Generative adversarial Networks (with code in TensorFlow)

Important Resources/models:

Panax 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)

Resnext aggregated residual transformations for deep neural Networks)

MultiPath Network Training Code

Important architecture and development libraries:

TensorFlow by Google [C + + and CUDA]

Wuyi Caffe by Berkeley Vision and Learning Center (BVLC) [c++][installation instructions]

Keras by François Chollet [Python]

Microsoft cognitive TOOLKIT-CNTK [C + +]

MXNet adapted by Amazon [C + +]

Torch by Collobert, Kavukcuoglu & Clement Farabet, widely used by Facebook [Lua]

Convnetjs by Andrej Karpathy [JavaScript]

Theano by Universitéde Montréal [Python]

Deeplearning4j by startup Skymind [Java]

Paddle by Baidu [C + +]

Scalable Sparse Tensor Network Engine (Dsstne) by Amazon [C + +]

Neon by Nervana Systems [Python & Sass]

Chainer [Python]

H2O [Java]

Brainstorm by Istituto dalle Molle di studi sull ' Intelligenza artificiale (Idsia) [Python]

Matconvnet by Andrea Vedaldi [Matlab]

Link version article download address:

Link: https://pan.baidu.com/s/1dGpAC97 Password: T4DD

Highlights of the past period recommended:

MIT-2018 latest automatic driving video course sharing

How to choose a suitable GPU card in deep learning some experience and suggestions to share

< teaching materials recommendation > Prml_ pattern Recognition and machine learning

2017 Montreal deep learning Summer school ppt sharing (with 2016 conference video address)

Optimization Strategy 5 Label Smoothing REGULARIZATION_LSR principle Analysis

Pure Dry 7 Domain Adaptation video tutorial (with PPT) and classic paper sharing