Awesome系列

• Awesome Machine Learning
• Awesome Deep Learning
• Awesome TensorFlow
• Awesome TensorFlow Implementations
• Awesome Torch
• Awesome Computer Vision
• Awesome Deep Vision
• Awesome RNN
• Awesome NLP
• Awesome AI
• Awesome Deep Learning Papers
• Awesome 2vec

Deep Learning

• [Book] Neural Networks and Deep Learning中文翻译(不完整): 神经网络与深度学习第五章中文翻译: [译] 第五章 深度神经网络为何很难训练
• [Book] Deep Learning - MIT Press
• [Course] Deep Learning - Udacity
• [Course] Machine Learning by Andrew Ng - Coursera | 课程资料整理 @ zhwhong
• [Course] Convolutional Neural Networks for Visual Recognition(CS231n) | 课程资料整理 @ zhwhong
• [Course] Deep Learning for Natural Language Processing(CS224d) | 课程资料整理 @ zhwhong
• [View] Top Deep Learning Projects on Github
• [View] Deep Learning for NLP resources
• [View] 资源 | 深度学习资料大全：从基础到各种网络模型
• [View] 深度学习新星：GAN的基本原理、应用和走向
• [Book] 推荐 | 九本不容错过的深度学习和神经网络书籍

Frameworks

• TensorFlow (by google)
• MXNet
• Torch (by Facebook)
• [Caffe (by UC Berkley)(Caffe | Deep Learning Framework)
• [Deeplearning4j(Open-source, Distributed Deep Learning for the JVM)
• Brainstorm
• Theano、Chainer、Marvin、Neon、ConvNetJS

TensorFlow

• 官方文档
• TensorFlow Tutorial
• TensorFlow 官方文档中文版
• TensorFlow Whitepaper
• [译] TensorFlow白皮书
• [API] API Document

入门教程

• [教程] Learning TensorFlow
• TensorFlow-Tutorials @ github (推荐)
• Awesome-TensorFlow (推荐)
• TensorFlow-Examples @ github
• tensorflow_tutorials @ github

分布式教程

• Distributed TensorFlow官方文档
• distributed-tensorflow-example @ github (推荐)
• DistributedTensorFlowSample @ github
• Parameter Server

Paper (Model)

CNN Nets

• LeNet
• AlexNet
• OverFeat
• NIN
• GoogLeNet
• Inception-V1
• Inception-V2
• Inception-V3
• Inception-V4
• Inception-ResNet-v2
• ResNet 50
• ResNet 101
• ResNet 152
• VGG 16
• VGG 19

注：图片来自 Github : TensorFlow-Slim image classification library)

额外参考：

[ILSVRC] 基于OverFeat的图像分类、定位、检测
[卷积神经网络-进化史] 从LeNet到AlexNet
[透析] 卷积神经网络CNN究竟是怎样一步一步工作的？
GoogLenet中，1X1卷积核到底有什么作用呢？
深度学习 — 反向传播(BP)理论推导

Object Detection

• R-CNN
• Fast R-CNN
• Faster R-CNN
• FCN
• R-FCN
• YOLO
• SSD

额外参考：

[Detection] CNN 之 "物体检测" 篇
计算机视觉中 RNN 应用于目标检测
Machine Learning 硬件投入调研

RNN & LSTM

• [福利] 深入理解 RNNs & LSTM 网络学习资料 @ zhwhong
• [RNN] Simple LSTM代码实现 & BPTT理论推导 @ zhwhong
• 计算机视觉中 RNN 应用于目标检测 @ zhwhong
• Understanding LSTM Networks @ colah | 中文翻译[简书] @ Not_GOD
• The Unreasonable Effectiveness of Recurrent Neural Networks @ Andrej Karpathy
• LSTM Networks for Sentiment Analysis (theano官网LSTM教程+代码)
• Recurrent Neural Networks Tutorial @ WILDML
• Anyone Can Learn To Code an LSTM-RNN in Python (Part 1: RNN) @ iamtrask

Stanford 机器学习课程整理

• [coursera 机器学习课程] Machine Learning by Andrew Ng @ zhwhong
• [斯坦福CS231n课程整理] Convolutional Neural Networks for Visual Recognition（附翻译，下载） @ zhwhong
• [斯坦福CS224d课程整理] Natural Language Processing with Deep Learning @ zhwhong
• [斯坦福CS229课程整理] Machine Learning Autumn 2016 @ zhwhong

Machine Learning 资料

原文：http://www.cnblogs.com/focusonepoint/p/7606030.html