超级大汇总！200多个最好的机器学习、NLP和Python教程

这篇文章包含了我目前为止找到的最好的教程内容。这不是一张罗列了所有网上跟 机器学习 相关教程的清单——不然就太冗长太重复了。我这里并没有包括那些质量一般的内容。我的目标是把能找到的最好的教程与 机器学习 和 自然语言处理 的延伸主题们连接到一起。

我这里指的“教程”，是指那些为了简洁地传授一个概念而写的介绍性内容。我尽量避免了教科书里的章节，因为它们涵盖了更广的内容，或者是研究论文，通常对于传授概念来说并不是很有帮助。如果是那样的话，为何不直接买书呢？当你想要学习一个基本主题或者是想要获得更多观点的时候，教程往往很有用。

我把这篇文章分为了四个部分： 机器学习 ， 自然语言处理 ，python和数学。在每个部分中我都列举了一些主题，但是因为材料的数量庞大，我不可能涉及到每一个主题。

如果你发现到我遗漏了哪些好的教程，请告诉我！我尽量把每个主题下的教程控制在五个或者六个，如果超过了这个数字就难免会有重复。每一个链接都包含了与其他链接不同的材料，或使用了不同的方式表达信息（例如：使用代码，幻灯片和长文），或者是来自不同的角度。

机器学习

Start Here with Machine Learning (machinelearningmastery.com)

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

Machine Learning is Fun! (medium.com/@ageitgey)

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

Rules of Machine Learning : Best Practices for ML Engineering(martin.zinkevich.org)

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

Machine Learning Crash Course: Part I, Part II, Part III ( Machine Learning at Berkeley)

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/

An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples (toptal.com)

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

A Gentle Guide to Machine Learning (monkeylearn.com)

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

Which machine learning algorithm should I use? (sas.com)

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

The Machine Learning Primer (sas.com)

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

Machine Learning Tutorial for Beginners (kaggle.com/kanncaa1)

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

激活和损失函数

Sigmoid neurons (neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap1.html#sigmoid_ neurons

What is the role of the activation function in a neural network? (quora.com)

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

Comprehensive list of activation functions in neural networks with pros/cons(stats.stackexchange.com)

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

Activation function s and it’s types-Which is better? (medium.com)

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

Making Sense of Logarithmic Loss (exegetic.biz)

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

Loss Functions (Stanford CS231n)

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

L1 vs. L2 Loss function (rishy.github.io)

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

The cross-entropy cost function (neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function

偏差

Role of Bias in Neural Network s (stackoverflow.com)

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

Bias Nodes in Neural Network s(makeyourownneuralnetwork.blogspot.com)

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

What is bias in artificial neural network? (quora.com)

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

感知 机

Perceptrons (neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap1.html# perceptron s

The Perception (natureofcode.com)

https://natureofcode.com/book/chapter-10-neural-networks/#chapter10_figure3

Single-layer Neural Network s (Perceptrons) (dcu.ie)

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

From Perceptrons to Deep Networks (toptal.com)

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

回归

Introduction to linear regression analysis (duke.edu)

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

Linear Regression (ufldl.stanford.edu)

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

Linear Regression (readthedocs.io)

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

Logistic Regression (readthedocs.io)

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

Simple Linear Regression Tutorial for Machine Learning (machinelearningmastery.com)

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

Logistic Regression Tutorial for Machine Learning (machinelearningmastery.com)

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

Softmax Regression (ufldl.stanford.edu)

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

梯度下降

Learning with gradient descent (neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap1.html#learning_with_gradient_descent

Gradient Descent (iamtrask.github.io)

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

How to understand Gradient Descent algorithm (kdnuggets.com)

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

An overview of gradient descent optimization algorithms(sebastianruder.com)

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

Optimization: Stochastic Gradient Descent (Stanford CS231n)

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

生成学习

Generative Learning Algorithms (Stanford CS229)

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

A practical explanation of a Naive Bayes classifier (monkeylearn.com)

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

支持向量机

An introduction to Support Vector Machines (SVM) (monkeylearn.com)

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

Support Vector Machines (Stanford CS229)

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

Linear classification: Support Vector Machine, Softmax (Stanford 231n)

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

深度学习

A Guide to Deep Learning by YN² (yerevann.com)

http://yerevann.com/a-guide-to-deep-learning/

Deep Learning Papers Reading Roadmap (github.com/floodsung)

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

Deep Learning in a Nutshell (nikhilbuduma.com)

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

A Tutorial on Deep Learning (Quoc V. Le)

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

What is Deep Learning? (machinelearningmastery.com)

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

What’s the Difference Between Artificial Intelligence , Machine Learning , and Deep Learning? (nvidia.com)

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

Deep Learning — The Straight Dope (gluon.mxnet.io)

https://gluon.mxnet.io/

优化和降维

Seven Techniques for Data Dimensionality Reduction (knime.org)

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

Principal components analysis (Stanford CS229)

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

Dropout : A simple way to improve neural networks (Hinton @ NIPS 2012)

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

How to train your Deep Neural Network (rishy.github.io)

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

长短期记忆（LSTM ）

A Gentle Introduction to Long Short-Term Memory Networks by the Experts(machinelearningmastery.com)

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

Understanding LSTM Networks (colah.github.io)

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

Exploring LSTM s (echen.me)

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

Anyone Can Learn To Code an LSTM -RNN in Python (iamtrask.github.io)

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

卷积神经网络

Introducing convolutional networks (neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap6.html#introducing_convolutional_networks

Deep Learning and Convolutional Neural Network s(medium.com/@ageitgey)

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

Conv Nets: A Modular Perspective (colah.github.io)

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

Understanding Convolutions (colah.github.io)

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

递归神经网络

Recurrent Neural Network s Tutorial (wildml.com)

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

Attention and Augmented Recurrent Neural Network s (distill.pub)

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

The Unreasonable Effectiveness of Recurrent Neural Network s(karpathy.github.io)

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

A Deep Dive into Recurrent Neural Nets (nikhilbuduma.com)

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

强化学习

Simple Beginner’s guide to Reinforcement Learning & its implementation(analyticsvidhya.com)

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

A Tutorial for Reinforcement Learning (mst.edu)

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

Learning Reinforcement Learning (wildml.com)

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

Deep Reinforcement Learning: Pong from Pixels (karpathy.github.io)

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

生成对抗网络 （GANs）

Adversarial Machine Learning (aaai18adversarial.github.io)

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

What’s a Generative Adversarial Network? (nvidia.com)

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

Abusing Generative Adversarial Networks to Make 8-bit Pixel Art(medium.com/@ageitgey)

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

An introduction to Generative Adversarial Networks (with code in Tensor Flow) (aylien.com)

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

Generative Adversarial Networks for Beginners (oreilly.com)

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

多任务学习

An Overview of Multi-Task Learning in Deep Neural Network s(sebastianruder.com)

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

自然语言处理

Natural Language Processing is Fun! (medium.com/@ageitgey)

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

A Primer on Neural Network Models for Natural Language Processing(Yoav Goldberg)

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

The Definitive Guide to Natural Language Processing (monkeylearn.com)

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

Introduction to Natural Language Processing (algorithmia.com)

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

Natural Language Processing Tutorial (vikparuchuri.com)

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

Natural Language Processing (almost) from Scratch (arxiv.org)

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

深度学习 和自然语言处理

Deep Learning applied to NLP (arxiv.org)

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

Deep Learning for NLP (without Magic) (Richard Socher)

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

Understanding Convolutional Neural Network s for NLP (wildml.com)

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

Deep Learning, NLP, and Representations (colah.github.io)

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

Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models (explosion.ai)

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

Understanding Natural Language with Deep Neural Network s Using Torch(nvidia.com)

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

Deep Learning for NLP with Pytorch (pytorich.org)                                            

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

词向量

Bag of Words Meets Bags of Popcorn (kaggle.com)

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

On word embeddings Part I, Part II, Part III (sebastianruder.com)

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

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

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

The amazing power of word vectors (acolyer.org)

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

word2vec Parameter Learning Explained (arxiv.org)

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

Word2Vec Tutorial — The Skip-Gram Model, Negative Sampling(mccormickml.com)

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

Attention and Memory in Deep Learning and NLP (wildml.com)

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

Sequence to Sequence Models (tensorflow.org)

https://www.tensorflow.org/tutorials/seq2seq

Sequence to Sequence Learning with Neural Network s (NIPS 2014)

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

Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences (medium.com/@ageitgey)

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

tf-seq2seq (google.github.io)

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

Python

Machine Learning Crash Course (google.com)

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

Awesome Machine Learning (github.com/josephmisiti)

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

7 Steps to Mastering Machine Learning With Python (kdnuggets.com)

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

An example machine learning notebook (nbviewer.jupyter.org)

http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb

Machine Learning with Python (tutorialspoint.com)

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

范例

How To Implement The Perceptron Algorithm From Scratch In Python(machinelearningmastery.com)

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

Implementing a Neural Network from Scratch in Python (wildml.com)

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

A Neural Network in 11 lines of Python (iamtrask.github.io)

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

Implementing Your Own k-Nearest Neighbour Algorithm Using Python(kdnuggets.com)

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

ML from Scatch (github.com/eriklindernoren)

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

Python Machine Learning (2nd Ed.) Code Repository (github.com/rasbt)

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

Scipy and numpy

Scipy Lecture Notes (scipy-lectures.org)

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

Python Numpy Tutorial (Stanford CS231n)

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

An introduction to Numpy and Scipy (UCSB CHE210D)

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

A Crash Course in Python for Scientists (nbviewer.jupyter.org)

http://nbviewer.jupyter.org/gist/rpmuller/5920182#ii.-numpy-and-scipy

scikit-learn

PyCon scikit-learn Tutorial Index (nbviewer.jupyter.org)

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

scikit-learn Classification Algorithms (github.com/mmmayo13)

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

scikit-learn Tutorials (scikit-learn.org)

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

Abridged scikit-learn Tutorials (github.com/mmmayo13)

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

Tensor flow

Tensor flow Tutorials (tensorflow.org)

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

Introduction to Tensor Flow — CPU vs GPU (medium.com/@erikhallstrm)

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

Tensor Flow: A primer (metaflow.fr)

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

RNNs in Tensor flow (wildml.com)

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

Implementing a CNN for Text Classification in Tensor Flow (wildml.com)

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

How to Run Text Summarization with Tensor Flow (surmenok.com)

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

PyTorch

PyTorch Tutorials (pytorch.org)

http://pytorch.org/tutorials/

A Gentle Intro to PyTorch (gaurav.im)

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

Tutorial: Deep Learning in PyTorch (iamtrask.github.io)

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

PyTorch Examples (github.com/jcjohnson)

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

PyTorch Tutorial (github.com/MorvanZhou)

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

PyTorch Tutorial for Deep Learning Researchers (github.com/yunjey)

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

数学

Math for Machine Learning (ucsc.edu)

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

Math for Machine Learning (UMIACS CMSC422)

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

线性代数

An Intuitive Guide to Linear Algebra (betterexplained.com)

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

A Programmer’s Intuition for Matrix Multiplication (betterexplained.com)

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

Understanding the Cross Product (betterexplained.com)

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

Understanding the Dot Product (betterexplained.com)

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

Linear Algebra for Machine Learning (U. of Buffalo CSE574)

http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/LinearAlgebra.pdf

Linear algebra cheat sheet for deep learning (medium.com)

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

Linear Algebra Review and Reference (Stanford CS229)

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

概率

Understanding Bayes Theorem With Ratios (betterexplained.com)

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

Review of Probability Theory (Stanford CS229)

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

Probability Theory Review for Machine Learning (Stanford CS229)

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

Probability Theory (U. of Buffalo CSE574)

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

Probability Theory for Machine Learning (U. of Toronto CSC411)

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

微积分

How To Understand Derivatives: The Quotient Rule, Exponents, and Logarithms (betterexplained.com)

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

How To Understand Derivatives: The Product, Power & Chain Rules(betterexplained.com)

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

Vector Calculus: Understanding the Gradient (betterexplained.com)

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

Differential Calculus (Stanford CS224n)

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

Calculus Overview (readthedocs.io)

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