# awesome-machine-learning

**Repository Path**: WSWZHHH/awesome-machine-learning

## Basic Information

- **Project Name**: awesome-machine-learning
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 33
- **Created**: 2025-06-19
- **Last Updated**: 2025-06-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# awesome-machine-learning
机器学习相关学习资料-书籍-学习代码。(更新中)

# 网址资源
* [GitHub使用教程](https://mp.weixin.qq.com/s/B5DLeM0TqIodfKEW2AAP5g)

## 博客
* [Open AI](https://blog.openai.com/)
* [DeepMind](https://deepmind.com/blog/?category=research)
* [Facebook AI Research博客](https://research.fb.com/blog/)
* [FerencHuszár的博客(剑桥的博士)](http://www.inference.vc/)
* [Distill致力于清晰地解释机器学习](https://distill.pub/)
* [Graduate Descent(深度学习的自然语言处理)](http://timvieira.github.io/blog/)
* [Adit Deshpande的博客(机器学习和深度学习)](https://adeshpande3.github.io/)
* [Andrew Trask的博客-神经网络及其解释和实现](http://iamtrask.github.io/)
* [特斯拉的人工智能总监Andrej Karpathy的博客](http://karpathy.github.io/)
* [Denny Britz的博客(Google Brain团队的前员工)](http://www.wildml.com/)
* [Sebastian Ruder(文本分析初创公司Aylien的研究科学家)](http://ruder.io/)
* [Colah的博客 Olah旨在以简单的方式解释神经网络的复杂功能](http://colah.github.io/)
* [BAIR博客旨在传播BAIR在人工智能研究方面的研究成果，观点和最新情况](http://bair.berkeley.edu/blog/)

## Github资源

### 书籍代码资料
* [书籍源码实现：《TensorFlow实战》](https://github.com/terrytangyuan/tensorflow-in-practice-code)
* [书籍《Python计算机视觉中译本》代码实例](https://github.com/willard-yuan/pcv-book-code)
* [书籍《统计学习方法-李航》一书中所有算法实现](https://github.com/WenDesi/lihang_book_algorithm)
* [书籍源码《Hands-On Transfer Learning with Python》](https://github.com/dipanjanS/hands-on-transfer-learning-with-python)
* [书籍《Machine-Learning 《Machine Learning in Action》-中文版 代码](https://github.com/pbharrin/machinelearninginaction)
* [书籍《Deep Learning《 Deep Learning With Python - 中文版》.pdf 代码](https://github.com/fchollet/deep-learning-with-python-notebooks)
* [书籍《Deep-Learning 《 Applied Deep Learning with Python》.pdf 》代码](https://github.com/TrainingByPackt/Applied-Deep-Learning-with-Python)
* [书籍 《Pattern recognition and machine learing_马春鹏翻译版.pdf 》MatLab代码](https://github.com/PRML/PRMLT) ; [python版](https://github.com/ctgk/PRML)


### 面试资源
* [DeepLearning-500-questions](https://github.com/scutan90/DeepLearning-500-questions)
* [2019-Autumn-recruitment-experience](https://github.com/zslomo/2019-Autumn-recruitment-experience)
* [机器学习资源 Machine learning Resources](https://github.com/allmachinelearning/MachineLearning)
* [机器学习&深度学习网站资源汇总（Machine Learning Resources）](https://github.com/howie6879/mlhub123)
* [2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese)

### GitHub其他awesome资源
* [Awesome-pytorch-list](https://github.com/bharathgs/Awesome-pytorch-list)
* [awesome-pytorch-scholarship](https://github.com/arnas/awesome-pytorch-scholarship)
* [Awesome Python Applications](https://github.com/mahmoud/awesome-python-applications)
* [awesome-deeplearning-resources](https://github.com/endymecy/awesome-deeplearning-resources)
* [TensorFlow - A curated list of dedicated resources](https://github.com/jtoy/awesome-tensorflow)
* [Awesome Object Detection based on handong1587 github](https://github.com/amusi/awesome-object-detection)
* [awesome-deep-learning  A curated list of awesome Deep Learning tutorials, projects and communities.](https://github.com/ChristosChristofidis/awesome-deep-learning)




## 视频资源
### TensorFlow资源
* [TF Girls 修炼指南](https://www.youtube.com/watchv=TrWqRMJZU8A&list=PLwY2GJhAPWRcZxxVFpNhhfivuW0kX15yG&index=2)
* [炼数成金Tensorflow公开课](https://www.youtube.com/watchv=eAtGqz8ytOI&list=PLjSwXXbVlK6IHzhLOMpwHHLjYmINRstrk)
* [Dan Does Data: Tensor Flow](http://bit.ly/1OX8s8Y)
* [谷歌Tensorflow官网上的视频教程](https://developers.google.cn/machine-learning/crash-course/)
* [Tensorflow与深度学习（人工智能）-初学篇 (Martin Görner)](https://www.youtube.com/watch?v=vq2nnJ4g6N0)
* [斯坦福大学Tensorflow系列的课程](https://www.youtube.com/watch?v=g-EvyKpZjmQ&index=1&list=PLIDllPt3EQZoS8gCP3cw273Cq9puuPLTg) ；[课程主页](http://web.stanford.edu/class/cs20si/index.html) ；[课程实战代码](https://github.com/chiphuyen/stanford-tensorflow-tutorials)
* [Julia Ferraioli, Amy Unruh, Eli Bixby - Diving into Machine Learning through TensorFlow - PyCon 2016](https://www.youtube.com/watch?v=GZBIPwdGtkk&t=125s)
* [Tensorflow代码练习](https://github.com/terryum/TensorFlow_Exercises)
* [TensorFlow官方中文教程](https://tensorflow.google.cn/tutorials/?hl=zh-cn)
* [从Tensorflow基础知识到有趣的项目应用](https://github.com/pkmital/tensorflow_tutorials)
* [构建您的第一款TensorFlow Android应用程序](https://omid.al/posts/2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html)
* [使用Jupyter Notebook运行的TensorFlow教程](https://github.com/sjchoi86/Tensorflow-101)
* [Simple and ready-to-use tutorials for TensorFlow](https://github.com/open-source-for-science/TensorFlow-Course#why-use-tensorflow)
* [tensorflow实战练习，包括强化学习、推荐系统、nlp等](https://github.com/princewen/tensorflow_practice)
* [TensorFlow Tutorial and Examples for Beginners with Latest APIs](https://github.com/aymericdamien/TensorFlow-Examples)


### 深度学习视频
* [台湾国立大学李宏毅教程深度学习的课程](https://www.bilibili.com/video/av9770302/)


## 在线书籍资源
* [《动手学 深度学习》](https://zh.diveintodeeplearning.org/index.html)
* [Deep Learning Book Chinese Translation](https://github.com/exacity/deeplearningbook-chinese)
[Companion webpage to the book "Mathematics For Machine Learning"](https://mml-book.com)

## 深度学习实战资源

* [免费的编程中文书籍索引](https://github.com/justjavac/free-programming-books-zh_CN)
* [ILearnDeepLearning.py](https://github.com/SkalskiP/ILearnDeepLearning.py)
* [All Algorithms implemented in Python](https://github.com/TheAlgorithms/Python)
* [【国赛】【美赛】数学建模相关算法 MATLAB实现 ](https://github.com/HuangCongQing/Algorithms_MathModels)
* [AI比赛相关](https://github.com/HuangCongQing/AI_competitions)
* [Text to image synthesis using thought vectors](https://github.com/paarthneekhara/text-to-image)
* [Generative Adversarial Text to Image Synthesis](https://github.com/zsdonghao/text-to-image)
* [Code for paper "Plug and Play Generative Networks"](https://github.com/Evolving-AI-Lab/ppgn)
* [TensorFlow Implementation of "Show, Attend and Tell" ](https://github.com/yunjey/show-attend-and-tell)
* [Estimate 3D face pose by a Convolutional Neural Network](https://github.com/fengju514/Face-Pose-Net)
* [practicalAI : A practical approach to learning machine learning.](https://github.com/GokuMohandas/practicalAI/)
* [Convolutional Neural Network for Text Classification in Tensorflow ](https://github.com/dennybritz/cnn-text-classification-tf)
* [A recurrent neural network for generating little stories about images ](https://github.com/ryankiros/neural-storyteller)
* [Generative Handwriting using LSTM Mixture Density Network with TensorFlow ](https://github.com/hardmaru/write-rnn-tensorflow)
* [Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow](https://github.com/matterport/Mask_RCNN)
* [Machine learning resources，including algorithm, paper, dataset, example and so on.](https://github.com/csuldw/MachineLearning)
* [Simple embedding based text classifier inspired by fastText, implemented in tensorflow ](https://github.com/apcode/tensorflow_fasttext)
* [A PyTorch implementation of the architecture of Mask RCNN, serves as an introduction to working with PyTorch](https://github.com/wannabeOG/Mask-RCNN)
* [Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices](https://github.com/thtrieu/darkflow)

## 论文源码复现
* [Visualizing the Loss Landscape of Neural Nets. NIPS](https://github.com/tomgoldstein/loss-landscape)
* [Adversarially Parameterized Optimization for 3D Human Pose Estimation](https://github.com/jackd/adversarially_parameterized_optimization)
* [NSGA-NET: A Multi-Objective Genetic Algorithm for Neural Architecture Search](https://github.com/ianwhale/nsga-net)
* [PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models](https://github.com/r9y9/deepvoice3_pytorch)


## Paper
* [Deep Learning Papers Reading Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap)
* [Activation Functions: Comparison of Trends in Practice and Research for Deep Learning](https://arxiv.org/abs/1811.03378)
* [Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs](https://arxiv.org/abs/1804.04438)