非常全面！200种机器学习教程汇总！

我将这篇文章分为四个部分：机器学习，NLP，Python和数学。我在每个部分都包含了一些主题，但由于机器学习是一个非常复杂的学科，我不可能包含所有可能的主题。

如果有很好的教程你知道我错过了，请告诉我！我将继续完善这个学习教程。我在挑选这些链接的时候，都试图保证每个链接应该具有与其他链接不同的材料或以不同的方式呈现信息（例如，代码与幻灯片）或从不同的角度。

机器学习

从机器学习入手

https://machinelearningmastery.com/start-here/

机器学习很有趣！

https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471

机器学习规则：ML工程的最佳实践

http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

机器学习速成课程：第一部分，第二部分，第三部分（伯克利机器学习）

https://ml.berkeley.edu/blog/2016/11/06/tutorial-1/

https://ml.berkeley.edu/blog/2016/12/24/tutorial-2/

https://ml.berkeley.edu/blog/2017/02/04/tutorial-3/

机器学习理论及其应用简介：用一个小例子进行视觉教程

https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer

机器学习的简单指南

https://monkeylearn.com/blog/a-gentle-guide-to-machine-learning/

我应该使用哪种机器学习算法？

https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/

机器学习入门

https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/machine-learning-primer-108796.pdf

初学者机器学习教程

https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners

激活函数和Dropout函数

Sigmoid神经元

http://neuralnetworksanddeeplearning.com/chap1.html

激活函数在神经网络中的作用是什么？

https://www.quora.com/What-is-the-role-of-the-activation-function-in-a-neural-network

神经网络中常见的激活函数的优缺点比较列表

https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-cons

激活函数及其类型对比

https://medium.com/towards-data-science/activation-functions-and-its-types-which-is-better-a9a5310cc8f

理解对数损失

http://www.exegetic.biz/blog/2015/12/making-sense-logarithmic-loss/

损失函数（斯坦福CS231n）

http://cs231n.github.io/neural-networks-2/

L1与L2损失函数

http://rishy.github.io/ml/2015/07/28/l1-vs-l2-loss/

交叉熵成本函数

http://neuralnetworksanddeeplearning.com/chap3.html

偏差（bias）

偏差在神经网络中的作用

https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks/2499936

神经网络中的偏差节点

http://makeyourownneuralnetwork.blogspot.com/2016/06/bias-nodes-in-neural-networks.html

什么是人工神经网络的偏差？

https://www.quora.com/What-is-bias-in-artificial-neural-network

感知器

感知器

http://neuralnetworksanddeeplearning.com/chap1.html

感知器

http://natureofcode.com/book/chapter-10-neural-networks/

单层神经网络（感知器）

http://computing.dcu.ie/~humphrys/Notes/Neural/single.neural.html

从Perceptrons到Deep Networks

https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

回归

线性回归分析介绍

http://people.duke.edu/~rnau/regintro.htm

线性回归

http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/

线性回归

http://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html

Logistic回归

http://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html

机器学习的简单线性回归教程

http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/

机器学习的Logistic回归教程

http://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/

Softmax回归

http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression/

梯度下降

在梯度下降中学习

http://neuralnetworksanddeeplearning.com/chap1.html

梯度下降

http://iamtrask.github.io/2015/07/27/python-network-part2/

如何理解梯度下降算法

http://www.kdnuggets.com/2017/04/simple-understand-gradient-descent-algorithm.html

梯度下降优化算法概述

http://sebastianruder.com/optimizing-gradient-descent/

优化：随机梯度下降（斯坦福CS231n）

http://cs231n.github.io/optimization-1/

生成学习（GenerativeLearning）

生成学习算法（斯坦福CS229）

http://cs229.stanford.edu/notes/cs229-notes2.pdf

朴素贝叶斯分类器的实用解释

https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/

https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/

支持向量机

支持向量机（SVM）简介

https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/

支持向量机（斯坦福CS229）

http://cs229.stanford.edu/notes/cs229-notes3.pdf

线性分类：支持向量机，Softmax

http://cs231n.github.io/linear-classify/

反向传播

你应该了解的backprop

（medium.com/@karpathy）

你能给出神经网络反向传播算法的直观解释吗？

https://github.com/rasbt/python-machine-learning-book/blob/master/faq/visual-backpropagation.md

反向传播算法的工作原理

http://neuralnetworksanddeeplearning.com/chap2.html

通过时间反向传播和消失的渐变

http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/

http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/

时间反向传播的简单介绍

http://machinelearningmastery.com/gentle-introduction-backpropagation-time/

反向传播，直觉（斯坦福CS231n）

http://cs231n.github.io/optimization-2/

深度学习

YN²深度学习指南

http://cs231n.github.io/optimization-2/

深度学习论文阅读路线图

https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap

Nutshell中的深度学习

http://nikhilbuduma.com/2014/12/29/deep-learning-in-a-nutshell/

深度学习教程

http://ai.stanford.edu/~quocle/tutorial1.pdf

什么是深度学习？

http://machinelearningmastery.com/what-is-deep-learning/

人工智能，机器学习和深度学习之间有什么区别？

https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

深度学习–简单介绍

https://gluon.mxnet.io/

最优化和降维

数据降维减少的七种技术

https://www.knime.org/blog/seven-techniques-for-data-dimensionality-reduction

主成分分析（斯坦福CS229）

http://cs229.stanford.edu/notes/cs229-notes10.pdf

Dropout：一种改善神经网络的简单方法http://videolectures.net/site/normal_dl/tag=741100/nips2012_hinton_networks_01.pdf

如何训练你的深度神经网络?

http://rishy.github.io/ml/2017/01/05/how-to-train-your-dnn/

长短期记忆（LSTM）

长短期记忆网络的通俗介绍

http://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/

了解LSTM 神经网络Networks

http://colah.github.io/posts/2015-08-Understanding-LSTMs/

探索LSTM

http://blog.echen.me/2017/05/30/exploring-lstms/

任何人都可以学习用Python编写LSTM-RNN

http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

卷积神经网络（CNN）

卷积网络介绍

http://neuralnetworksanddeeplearning.com/chap6.html

深度学习和卷积神经网络

https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721

Conv Nets：模块化视角

http://colah.github.io/posts/2014-07-Conv-Nets-Modular/

了解卷积

http://colah.github.io/posts/2014-07-Understanding-Convolutions/

递归神经网络（RNN）

递归神经网络教程

http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/

注意和增强的递归神经网络

http://distill.pub/2016/augmented-rnns/

递归神经网络的不合理有效性

http://karpathy.github.io/2015/05/21/rnn-effectiveness/

深入了解递归神经网络

http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/

http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/

强化学习

强化学习初学者入门及其实施指南

https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/

强化学习教程

https://web.mst.edu/~gosavia/tutorial.pdf

学习强化学习

http://www.wildml.com/2016/10/learning-reinforcement-learning/

深度强化学习：来自像素的乒乓球

http://karpathy.github.io/2016/05/31/rl/

生成对抗网络（GAN）

对抗机器学习简介

https://aaai18adversarial.github.io/slides/AML.pptx

什么是生成性对抗网络？

https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network/

滥用生成对抗网络制作8位像素艺术

https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7

Generative Adversarial Networks简介（TensorFlow中的代码）

http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/

初学者的生成对抗网络

https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners

多任务学习

深度神经网络中多任务学习概述

http://sebastianruder.com/multi-task/index.html

NLP

自然语言处理很有趣！

https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e

自然语言处理神经网络模型入门

http://u.cs.biu.ac.il/~yogo/nnlp.pdf

自然语言处理权威指南

https://monkeylearn.com/blog/the-definitive-guide-to-natural-language-processing/

自然语言处理简介

https://blog.algorithmia.com/introduction-natural-language-processing-nlp/

自然语言处理教程

http://www.vikparuchuri.com/blog/natural-language-processing-tutorial/

自然语言处理（NLP）来自Scratch

https://arxiv.org/pdf/1103.0398.pdf

深度学习和NLP

深度学习适用于NLP

https://arxiv.org/pdf/1703.03091.pdf

NLP的深度学习（没有魔法）

https://nlp.stanford.edu/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdf

了解NLP的卷积神经网络

http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/

深度学习、NLP、表示

http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/

最先进的NLP模型的新深度学习公式：嵌入、编码、参与、预测

https://explosion.ai/blog/deep-learning-formula-nlp

使用Torch深度神经网络进行自然语言处理

https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/

使用Pytorch进行深度学习NLP

http://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html

词向量

使用词袋模型解决电影评论分类

https://www.kaggle.com/c/word2vec-nlp-tutorial

词嵌入介绍第一部分，第二部分，第三部分

http://sebastianruder.com/word-embeddings-1/index.html

http://sebastianruder.com/word-embeddings-softmax/index.html

http://sebastianruder.com/secret-word2vec/index.html

词向量的惊人力量

https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/

word2vec参数学习解释

https://arxiv.org/pdf/1411.2738.pdf

Word2Vec教程- Skip-Gram模型，负抽样

http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/

http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/

编码器-解码器

深度学习和NLP中的注意力机制和记忆力模型

http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/

序列模型

tensorflow.org

使用神经网络进行序列学习

https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf

机器学习很有趣第五部分：深度学习的语言翻译和序列的魔力

https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa

如何使用编码器-解码器LSTM来回显随机整数序列

http://machinelearningmastery.com/how-to-use-an-encoder-decoder-lstm-to-echo-sequences-of-random-integers/

tf-seq2seq

https://google.github.io/seq2seq/

Python

机器学习速成课程

https://developers.google.com/machine-learning/crash-course/

令人敬畏的机器学习

https://github.com/josephmisiti/awesome-machine-learning

使用Python掌握机器学习的7个步骤

http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html

一个示例机器学习笔记

http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example Machine Learning Notebook.ipynb

使用Python进行机器学习

https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_quick_guide.htm

实战案例

如何在Python中从头开始实现感知器算法

http://machinelearningmastery.com/implement-perceptron-algorithm-scratch-python/

在Python中使用Scratch实现神经网络

http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/

使用11行代码在Python中实现神经网络

http://iamtrask.github.io/2015/07/12/basic-python-network/

使用Python实现你自己的k-Nearest Neighbor算法

http://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html

来自Scatch的ML

https://github.com/eriklindernoren/ML-From-Scratch

Python机器学习（第2版）代码库

https://github.com/rasbt/python-machine-learning-book-2nd-edition

Scipy和numpy

Scipy讲义

http://www.scipy-lectures.org/

Python Numpy教程

http://cs231n.github.io/python-numpy-tutorial/

Numpy和Scipy简介

https://engineering.ucsb.edu/~shell/che210d/numpy.pdf

Python中的科学家速成课程

http://nbviewer.jupyter.org/gist/rpmuller/5920182

http://nbviewer.jupyter.org/gist/rpmuller/5920182

scikit学习

PyCon scikit-learn教程索引

http://nbviewer.jupyter.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb

scikit-learn分类算法

https://github.com/mmmayo13/scikit-learn-classifiers/blob/master/sklearn-classifiers-tutorial.ipynb

scikit-learn教程

http://scikit-learn.org/stable/tutorial/index.html

简短的scikit-learn教程

https://github.com/mmmayo13/scikit-learn-beginners-tutorials

Tensorflow

Tensorflow教程

https://www.tensorflow.org/tutorials/

TensorFlow简介 - CPU与GPU

https://medium.com/@erikhallstrm/hello-world-tensorflow-649b15aed18c

TensorFlow

https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3

Tensorflow中的RNN

http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

在TensorFlow中实现CNN进行文本分类

http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/

如何使用TensorFlow运行文本摘要

http://pavel.surmenok.com/2016/10/15/how-to-run-text-summarization-with-tensorflow/

PyTorch

PyTorch教程

http://pytorch.org/tutorials/

PyTorch的简单介绍

http://blog.gaurav.im/2017/04/24/a-gentle-intro-to-pytorch/

教程：PyTorch中的深度学习

https://iamtrask.github.io/2017/01/15/pytorch-tutorial/

PyTorch示例

https://github.com/jcjohnson/pytorch-examples

PyTorch教程

https://github.com/MorvanZhou/PyTorch-Tutorial

深度学习研究人员的PyTorch教程

https://github.com/yunjey/pytorch-tutorial

数学

机器学习数学

https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf

机器学习数学

http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf

线性代数

线性代数直观指南

https://betterexplained.com/articles/linear-algebra-guide/

程序员对矩阵乘法的直觉

https://betterexplained.com/articles/matrix-multiplication/

了解Cross产品

https://betterexplained.com/articles/cross-product/

了解Dot产品

https://betterexplained.com/articles/vector-calculus-understanding-the-dot-product/

用于机器学习的线性代数（布法罗大学CSE574）http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/LinearAlgebra.pdf

用于深度学习的线性代数备忘单

https://medium.com/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c

线性代数评论与参考

http://cs229.stanford.edu/section/cs229-linalg.pdf

概率论

用比率理解贝叶斯定理

https://betterexplained.com/articles/understanding-bayes-theorem-with-ratios/

概率论入门

http://cs229.stanford.edu/section/cs229-prob.pdf

机器学习的概率论教程

https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf

概率论（布法罗大学CSE574）

http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/Probability-Theory.pdf

机器学习的概率论（多伦多大学CSC411）

http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial1.pdf

微积分

如何理解导数：商数规则，指数和对数

https://betterexplained.com/articles/how-to-understand-derivatives-the-quotient-rule-exponents-and-logarithms/

如何理解导数：产品，动力和链条规则

（betterexplained.com）

https://betterexplained.com/articles/derivatives-product-power-chain/

矢量微积分：了解渐变

https://betterexplained.com/articles/vector-calculus-understanding-the-gradient/

微分学（斯坦福CS224n）

http://web.stanford.edu/class/cs224n/lecture_notes/cs224n-2017-review-differential-calculus.pdf

微积分概述

http://ml-cheatsheet.readthedocs.io/en/latest/calculus.html