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 中文翻譯(不完整): 神經(jīng)網(wǎng)絡(luò)與深度學(xué)習(xí) 第五章中文翻譯: [譯] 第五章 深度神經(jīng)網(wǎng)絡(luò)為何很難訓(xùn)練
- [Book] Deep Learning - MIT Press
- [Book] Pattern Recognition and Machine Learning (Bishop) | 豆瓣 | PRML & DL筆記 | GitBook
- [Course] Deep Learning - Udacity
- [Course] Machine Learning by Andrew Ng - Coursera | 課程資料整理 @ zhwhong
- [Course] Convolutional Neural Networks for Visual Recognition(CS231n) | 課程資料整理 @ zhwhong
- [Course] Deep Learning for Natural Language Processing(CS224d) | 課程資料整理 @ zhwhong
- [View] Top Deep Learning Projects on Github
- [View] Deep Learning for NLP resources
- [View] 資源 | 深度學(xué)習(xí)資料大全：從基礎(chǔ)到各種網(wǎng)絡(luò)模型
- [View] Paper | DL相關(guān)論文中文翻譯
- [View] 深度學(xué)習(xí)新星：GAN的基本原理、應(yīng)用和走向
- [View] 推薦 | 九本不容錯(cuò)過(guò)的深度學(xué)習(xí)和神經(jīng)網(wǎng)絡(luò)書(shū)籍
- [View] Github好東西傳送門 --> 深度學(xué)習(xí)入門與綜述資料
Frameworks
- TensorFlow (by google)
- MXNet
- Torch (by Facebook)
- [Caffe (by UC Berkley)(http://caffe.berkeleyvision.org/)
- [Deeplearning4j(http://deeplearning4j.org)
- Brainstorm
- Theano、Chainer微宝、Marvin良狈、Neon炼邀、ConvNetJS
TensorFlow
- 官方文檔
- TensorFlow Tutorial
- TensorFlow 官方文檔中文版
- TensorFlow Whitepaper
- [譯] TensorFlow白皮書(shū)
- [API] API Document
入門教程
- [教程] Learning TensorFlow
- TensorFlow-Tutorials @ github (推薦)
- Awesome-TensorFlow (推薦)
- TensorFlow-Examples @ github
- tensorflow_tutorials @ github
分布式教程
- Distributed TensorFlow官方文檔
- distributed-tensorflow-example @ github (推薦)
- 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：圖片來(lái)自 Github : TensorFlow-Slim image classification library)
額外參考：
- [卷積神經(jīng)網(wǎng)絡(luò)-進(jìn)化史] 從LeNet到AlexNet
- [透析] 卷積神經(jīng)網(wǎng)絡(luò)CNN究竟是怎樣一步一步工作的？
- GoogLenet中厦章，1X1卷積核到底有什么作用呢？
- 深度學(xué)習(xí) — 反向傳播(BP)理論推導(dǎo)
- 無(wú)痛的機(jī)器學(xué)習(xí)第一季目錄 - 知乎
Object Detection
額外參考：
RNN & LSTM
- [福利] 深入理解 RNNs & LSTM 網(wǎng)絡(luò)學(xué)習(xí)資料 @ zhwhong
- [RNN] Simple LSTM代碼實(shí)現(xiàn) & BPTT理論推導(dǎo) @ zhwhong
- 計(jì)算機(jī)視覺(jué)中 RNN 應(yīng)用于目標(biāo)檢測(cè) @ zhwhong
- [推薦] Understanding LSTM Networks @ colah | 理解LSTM網(wǎng)絡(luò)[簡(jiǎn)書(shū)] @ Not_GOD
- The Unreasonable Effectiveness of Recurrent Neural Networks @ Andrej Karpathy
- LSTM Networks for Sentiment Analysis (theano官網(wǎng)LSTM教程+代碼)
- Recurrent Neural Networks Tutorial @ WILDML
- Anyone Can Learn To Code an LSTM-RNN in Python (Part 1: RNN) @ iamtrask
Stanford 機(jī)器學(xué)習(xí)課程整理
- [coursera 機(jī)器學(xué)習(xí)課程] Machine Learning by Andrew Ng @ zhwhong
- [斯坦福CS231n課程整理] Convolutional Neural Networks for Visual Recognition（附翻譯照藻，下載） @ zhwhong
- [斯坦福CS224d課程整理] Natural Language Processing with Deep Learning @ zhwhong
- [斯坦福CS229課程整理] Machine Learning Autumn 2016 @ zhwhong
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