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Deep-Learning

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深度学习/人工智能/机器学习资料汇总(Deep Learning/Artificial Intelligent/Machine Learning) 持续更新……

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# TIPS
- 🔥🔥🔥🔥🔥🔥🔥🔥 因工作繁忙，更新有延迟。公众号【久哥传奇】更方便 🔥🔥🔥🔥🔥🔥🔥🔥
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# Deep Learning【精选更新】
- 🔥 New：[10款推荐系统仿真器（模拟平台）汇总和点评](https://blog.csdn.net/baishuiniyaonulia/article/details/113914843)
- 
- 🔥 New：[不怕学不够，只怕不够学：这份MIT最新深度学习入门公开课请收好](https://mp.weixin.qq.com/s/ADWu9IL3PTBkwXLckuxAig)
- 🔥 New：[如何规划和启动你的ML/DL项目](https://blog.floydhub.com/structuring-and-planning-your-machine-learning-project/)
- 🔥 New：[马斯克星链正式上线！装个UFO ，来测星链互联网下载速度](https://mp.weixin.qq.com/s/14Ehw3GIxx4VEMJy0W2igQ)
- 🔥 New：[吴恩达直击灵魂叩问：AI社区最亟待解决的问题是什么？](https://mp.weixin.qq.com/s/XzihLduiM-vMJWrNte189g)

- 🔥 New：本期新增神经网络与深度学习、ArXiv论文及代码、深度学习速查表及笔试面试精选等内容

## 一 Python篇
### 基础环境

- 📦 Anaconda
* [最省心的Python版本和第三方库管理——初探Anaconda](https://zhuanlan.zhihu.com/p/25198543)

### 开发语言

* [从0到1，Python Web开发的进击之路](https://zhuanlan.zhihu.com/p/25038203)
* [从0到1，Python异步编程的演进之路](https://zhuanlan.zhihu.com/p/25228075)
* [国外著名python在线编程学习网站](https://www.codecademy.com/)
* [国内Python在线学习网站](http://www.runoob.com/python/python-tutorial.html)
* [以撸代码的形式学习Python](https://github.com/xianhu/LearnPython)
* 🔥 New:[Move: A Language With Programmable
Resources](https://developers.libra.org/docs/assets/papers/libra-move-a-language-with-programmable-resources.pdf)

### 实例练习

* [天猫双12爬虫（福利：266万条商品数据免费下载）](https://zhuanlan.zhihu.com/p/24312829)
* [新浪微博爬虫分享（一天可抓取 1300 万条数据）](http://blog.csdn.net/bone_ace/article/details/50903178)
* [如何用100行Python代码做出魔性声控游戏“八分音符酱”](https://zhuanlan.zhihu.com/p/25499306)
* [50行代码爬网页图片一例](https://zhuanlan.zhihu.com/p/28680797)
* 🔥 New:[Libra -- My First Transaction](https://developers.libra.org/docs/my-first-transaction)

### 爬虫框架

* [Scrapy, a fast high-level web crawling & scraping framework for Python.](https://github.com/scrapy/scrapy)
* [admin ui for scrapy/open source scrapinghub](https://github.com/DormyMo/SpiderKeeper)
* [Visual scraping for Scrapy](https://github.com/scrapinghub/portia)
* [简单易用的Python爬虫框架](https://github.com/xianhu/PSpider)
* [数据科学家的完整学习路径（Python版）](https://zhuanlan.zhihu.com/p/23229114)

## 二 深度学习篇
### 深度学习 📚

* [【经典】机器学习必看的5张图](https://zhuanlan.zhihu.com/p/37288569) *机器学习全景图*
* [🔥深度学习/机器学习必备速查表](https://github.com/kailashahirwar/cheatsheets-ai) *11767 top stars*
* [神经网络与深度学习导引（吴恩达）](https://zhuanlan.zhihu.com/p/29045731)
* [🔥【入门推荐】10行命令感受机器学习的神奇（0基础小白适用）](https://zhuanlan.zhihu.com/p/27303650)
* [深度学习入门信息汇总-知乎](https://www.zhihu.com/question/26006703)
* [如何用70行Java代码实现深度神经网络算法](http://geek.csdn.net/news/detail/56086)
* [【经典书籍】Deep Learning Book Chinese Translation](https://github.com/exacity/deeplearningbook-chinese)
* [驾驭AI：好的数据集是成功的一半](https://zhuanlan.zhihu.com/p/33782177) *如何迈出第一步*
* [快速搭建有趣的人工智能应用](https://zhuanlan.zhihu.com/p/33378716) *未充分训练的模型*
* [Google Cloud AutoML初体验](https://zhuanlan.zhihu.com/p/33112756) *解决人工智能和机器学习的高门槛，降低高使用成本*
* [卷积神经网络简介](https://zhuanlan.zhihu.com/p/25249694)

### 算法概述
* [机器学习基础算法](https://exacity.github.io/deeplearningbook-chinese/Chapter5_machine_learning_basics/)
*(线性回归、容量/过拟合/欠拟合、最大似然估计、偏差和方差、贝叶斯统计、监督学习算法、无监督学习算法、随机梯度下降、正则化等)*
* [随机森林算法概述](http://www.cnblogs.com/maybe2030/p/4585705.html)
* [BP神经网络算法概述](http://blog.csdn.net/zhongkejingwang/article/details/44514073)
* [监督机器学习的优化方法：从线性模型到深度学习](https://arxiv.org/abs/1706.10207)
* [Adam：随机优化的一种方法](https://arxiv.org/abs/1412.6980)
* [训练神经网络的5种算法](https://www.neuraldesigner.com/blog/5_algorithms_to_train_a_neural_network)

### 在线课程 🎥

*(推荐Michael Nielsen及吴恩达视频课程，内容深入浅出。其中1是网易免费版，尚无习题，如需要可在链接2注册获取。)*

* [🔥Michael Nielsen:Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)
* [🔥吴恩达给你的人工智能第一课【初学推荐】](http://mooc.study.163.com/smartSpec/detail/1001319001.htm?forcelogin=true&edusave=1)
* [Deep Learning Specialization Master Deep Learning, and Break into AI【原版附带习题】](https://www.coursera.org/specializations/deep-learning#courses)
* [斯坦福监督学习课程](http://ufldl.stanford.edu/tutorial/supervised/LogisticRegression/)
* [斯坦福李飞飞-深度学习计算机视觉](http://study.163.com/course/courseMain.htm?courseId=1003223001)
* [AI轻松学习版(短视频）](https://zhuanlan.zhihu.com/p/47736351)

### 研究论文 📓

* [Artificial Intelligence(人工智能)](https://arxiv.org/list/cs.AI/recent)
* [Computer Vision and Pattern Recognition(计算机视觉和模式识别)](https://arxiv.org/list/cs.CV/recent)
* [awesome-deep-learning-papers:The most cited deep learning papers](https://github.com/terryum/awesome-deep-learning-papers)
* [Deep Learning papers reading roadmap for anyone](https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap)
* [awesome-deep-learning:A curated list of awesome Deep Learning tutorials, projects and communities.](https://github.com/ChristosChristofidis/awesome-deep-learning)
* [Efficient and Robust Automated Machine Learning](http://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf)
* [Microsoft COCO: Common Objects in Context](https://arxiv.org/pdf/1405.0312.pdf)
* [Speed/accuracy trade-offs for modern convolutional object detectors](https://arxiv.org/pdf/1611.10012.pdf)
#### GAN
* [Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/pdf/1611.07004.pdf)
* [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://arxiv.org/pdf/1711.11585.pdf)
* [Unpaired Image-to-Image Translation
using Cycle-Consistent Adversarial Networks](https://arxiv.org/pdf/1703.10593.pdf)
* [A Style-Based Generator Architecture for Generative Adversarial Networks](https://arxiv.org/pdf/1812.04948.pdf)
* [Generating Cats with StyleGAN on AWS SageMaker](https://devopstar.com/2019/02/25/generating-cats-with-stylegan-on-aws-sagemaker/)

### TensorFlow

* [TF:An open-source software library for Machine Intelligence](https://github.com/tensorflow/tensorflow)
* [TensorFlow Guide 中文开发手册](https://cloud.tencent.com/developer/doc/1241)
* [Swift for TensorFlow documentation repository.](https://github.com/tensorflow/swift)
* [Swift for TensorFlow Design Overview](https://github.com/tensorflow/swift/blob/master/docs/DesignOverview.md)
* [Using Swift for TensorFlow](https://github.com/tensorflow/swift/blob/master/Usage.md)

### Caffe

* [Caffe: a fast open framework for deep learning.](https://github.com/BVLC/caffe)

### Torch
* [Torch is a scientific computing framework for machine learning algorithms that puts GPUs first](https://github.com/torch/torch7)
* [Code and data for paper "Deep Photo Style Transfer"](https://github.com/luanfujun/deep-photo-styletransfer)

### keras

* [keras:Deep Learning library for TensorFlow and Theano](https://github.com/fchollet/keras)
* [keras-rl:Deep Reinforcement Learning for Keras](https://github.com/matthiasplappert/keras-rl)
* [小白上手第一步：hello world!](https://github.com/fastforwardlabs/keras-hello-world)
* [小白上手进击之：mnist](https://github.com/wxs/keras-mnist-tutorial/blob/master/MNIST%20in%20Keras.ipynb)

### Deepmind

* [DNC/L2L/..（Deepmind开源代码汇总）](https://deepmind.com/research/open-source/open-source-code/)
* [Github Index（Deepmind官方GitHub）](https://github.com/deepmind)
* [DNC:A TensorFlow implementation of the Differentiable Neural Computer.](https://github.com/deepmind/dnc)
* [sonnet:TensorFlow-based neural network library](https://github.com/deepmind/sonnet)
* [lab:A customisable 3D platform for agent-based AI research](https://github.com/deepmind/lab)
* [Deepmind org lab page](https://deepmind.com/blog/open-sourcing-deepmind-lab/)
* [L2L PAPER:Learning to learn by gradient descent by gradient descent](https://arxiv.org/pdf/1606.04474.pdf)
* [Google is finding ways to make money from Alphabet's DeepMind A.I. technology](https://www.cnbc.com/2018/03/31/how-google-makes-money-from-alphabets-deepmind-ai-research-group.html) *应用*

### 训练平台 📊
* **ImageNet**:[image-net:计算机视觉系统识别项目](http://www.image-net.org/) *目前世界上图像识别最大的数据库*
* **Kaggle**:[The Home of Data Science & Machine Learning](https://www.kaggle.com/) *Kaggle helps you learn, work, and play*
* **AutoML**:[CLOUD AUTOML ALPHA](https://cloud.google.com/automl/) *Train high quality custom machine learning models with minimum effort and machine learning expertise*
* **CustomVision.AI**:[Visual Intelligence Made Easy](https://www.customvision.ai/) *Microsoft Custom Vision Services*
* **oneclick**:[Use AI to Build AI](https://www.oneclick.ai/) *The faster and easier way to build high-performing customized AI for those who don't want to become an AI expert.*

### 辅助工具
* [ArXiv最新论文及GitHub代码大全](https://paperswithcode.com/sota) *704 leaderboards • 1038 tasks • 831 datasets • 10673 papers with code*
* [数据可视化分析工具汇总](https://zhuanlan.zhihu.com/p/25780330)
* [A powerful, interactive charting and visualization library for browser](http://echarts.baidu.com/examples.html)
* [Google机器学习可视化工具Facets：从全新角度观察数据](https://github.com/pair-code/facets)
* [Apache Superset (incubating) is a modern, enterprise-ready business intelligence web application](https://github.com/apache/incubator-superset)
* [CUDA在线学习](http://www.nvidia.cn/object/cuda_education_cn_old.html)
* [A distributed tracing system, and APM ( Application Performance Monitoring )](https://github.com/apache/incubator-skywalking)

### 场景应用 📈
* [垃圾邮件/情绪分析/智能客服等：Library for fast text representation and classification.](https://github.com/facebookresearch/fastText)
* [脸部识别：The world's simplest facial recognition api for Python and the command line.](https://github.com/ageitgey/face_recognition)
* [Android物体识别APP：Fast-RCNN and Scene Recognition using Caffe](https://github.com/tzutalin/Android-Object-Detection)
* [Google物体识别API：Object_Detection_Tensorflow_API](https://github.com/priya-dwivedi/Deep-Learning/blob/master/Object_Detection_Tensorflow_API.ipynb)
* [对于谷歌开源的TensorFlow Object Detection API视频物体识别系统实现教程](http://blog.csdn.net/xiaoxiao123jun/article/details/76605928)
* [Facebook AI 负责人：深度学习技术趋势报告（2016）](https://mp.weixin.qq.com/s/fgVcpZvzr0WE8eP0gSRQdw) *一文总结历史沿革*

### 笔面精选
* [深度学习500问](https://github.com/scutan90/DeepLearning-500-questions)

## 三 数学篇
### 导数/微积分

* [logistic等各类回归/线性分析/向量机](http://www.cnblogs.com/jerrylead/tag/Machine%20Learning/)

### 深度学习精简版

* [线性代数](https://exacity.github.io/deeplearningbook-chinese/Chapter2_linear_algebra/)
* [概率与信息论](https://exacity.github.io/deeplearningbook-chinese/Chapter3_probability_and_information_theory/)
* [数值计算](https://exacity.github.io/deeplearningbook-chinese/Chapter4_numerical_computation/)

## 四 产业篇
### 重大政策

* [【重磅！】国务院关于印发新一代人工智能发展规划的通知](http://www.gov.cn/zhengce/content/2017-07/20/content_5211996.htm)

### 重要进展
* [China is building a giant $2.1 billion research park dedicated to developing A.I.](https://www.cnbc.com/2018/01/03/china-is-building-a-giant-2-point-1-billion-ai-research-park.html)
* [当量子计算遇上人工智能](http://it.people.com.cn/n1/2018/0402/c1009-29901958.html)

### 产业新闻

* [人工智能：新技术的“双刃剑”](http://www.edu.cn/xxh/ji_shu_ju_le_bu/rgzn/201804/t20180402_1593248.shtml)
* [Five Ways Machine Learning Can Save Your Company From A Security Breach Meltdown](https://www.forbes.com/sites/louiscolumbus/2018/03/26/five-ways-machine-learning-can-save-your-company-from-a-security-breach-meltdown/#610afd7d2585)
* [全球100家代表性AI公司](https://www.jiqizhixin.com/articles/2018-01-10-5)
**相关
* [Facebook Unveils Cryptocurrency Libra as an Alternative to Bitcoin](https://www.wsj.com/articles/facebook-unveils-crypto-wallet-based-on-currency-libra-11560850141)
* [Libra:A simple global currency and financial infrastructure that empowers billions of people](https://libra.org/en-US/)
* [DeepNude的算法以及通用的Image-to-Image理论与实践研究](https://github.com/yuanxiaosc/DeepNude-an-Image-to-Image-technology)
* [一文读懂GAN前世今生：Generative Adversarial Networks - The Story So Far](https://blog.floydhub.com/gans-story-so-far/)

## 附：TOP深度学习项目 🔥

#### 最后更新（star5000+排序）: 2019.04.02 / 上次更新：2018.01.27

| Project Name| Stars | Description |
| ------- | ------ | ------ |
| [TensorFlow](https://github.com/tensorflow/tensorflow) | 124490 | Computation using data flow graphs for scalable machine learning.
| [TensorFlow Models](https://github.com/tensorflow/models) | 50630 |  Models built with TensorFlow.
| [Keras](https://github.com/fchollet/keras) | 39678 | Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on Theano and TensorFlow.
| [Caffe](https://github.com/BVLC/caffe) | 27691 | Caffe: a fast open framework for deep learning.
| [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples) | 30458 | TensorFlow tutorials and code examples for beginners.
| [Neural Style](https://github.com/jcjohnson/neural-style) | 16692 | Torch implementation of neural style algorithm.
| [CNTK](https://github.com/Microsoft/CNTK) | 15965 | Computational Network Toolkit (CNTK).
| [MXNet](https://github.com/dmlc/mxnet) | 16622 | Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more.
| [Deep Dream](https://github.com/google/deepdream) | 11871 | Deep Dream.
| [OpenAI Gym](https://github.com/openai/gym) | 16117 | A toolkit for developing and comparing reinforcement learning algorithms.
| [OpenFace](https://github.com/cmusatyalab/openface) | 11845 | Face recognition with deep neural networks.
| [ConvNet JS](https://github.com/karpathy/convnetjs) | 9600 | Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.
| [Magenta](https://github.com/tensorflow/magenta) | 12790 | Magenta: Music and Art Generation with Machine Intelligence
| [Roc AlphaGo](https://github.com/Rochester-NRT/RocAlphaGo) | 8918 | An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search" (Nature 529, 484-489, 28 Jan 2016).
| [Neural Doodle](https://github.com/alexjc/neural-doodle) | 9050 | Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.)
| [deeplearning4j](https://github.com/deeplearning4j/deeplearning4j) | 10560 | Deep Learning for Java, Scala & Clojure on Hadoop, Spark.
| [Theano](https://github.com/Theano/Theano) | 8732 | Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.
| [Torch](https://github.com/torch/torch7) | 8241 | Torch7, Deep Learning Library.
| [TFLearn](https://github.com/tflearn/tflearn) | 9002 | Deep learning library featuring a higher-level API for TensorFlow.
| [Char RNN](https://github.com/karpathy/char-rnn) | 9211 | Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch.
| [TensorFlow Playground](https://github.com/tensorflow/playground) | 8015 | Play with neural networks!
| [Synaptic](https://github.com/cazala/synaptic) | 6463 | architecture-free neural network library for node.js and the browser
| [Nupic](https://github.com/numenta/nupic) | 5864 | Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms.
| [Leaf](https://github.com/autumnai/leaf) | 5389 | Open Machine Intelligence Framework for Hackers.

# AT LAST
- 🔥🔥🔥：您已经看到了最后，更多精彩，请关注【未名小猪】吧：

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