200多個最好的機器學習、NLP和Python教程
ofollow,noindex">大資料 文摘出品
編譯：瓜瓜、Aileen
這篇文章包含了我目前為止找到的最好的教程內容。這不是一張羅列了所有網上跟 機器學習 相關教程的清單——不然就太冗長太重複了。我這裡並沒有包括那些質量一般的內容。我的目標是把能找到的最好的教程與機器學習和 自然語言處理 的延伸主題們連線到一起。
我這裡指的“教程”，是指那些為了簡潔地傳授一個概念而寫的介紹性內容。我儘量避免了教科書裡的章節，因為它們涵蓋了更廣的內容，或者是研究論文，通常對於傳授概念來說並不是很有幫助。如果是那樣的話，為何不直接買書呢？當你想要學習一個基本主題或者是想要獲得更多觀點的時候，教程往往很有用。
我把這篇文章分為了四個部分：機器學習，自然語言處理，python和數學。在每個部分中我都列舉了一些主題，但是因為材料的數量龐大，我不可能涉及到每一個主題。
如果你發現到我遺漏了哪些好的教程，請告訴我！我儘量把每個主題下的教程控制在五個或者六個，如果超過了這個數字就難免會有重複。每一個連結都包含了與其他連結不同的材料，或使用了不同的方式表達資訊（例如：使用程式碼，幻燈片和長文），或者是來自不同的角度。
機器學習
Start Here with Machine Learning (machinelearningmastery.com)
Start Here With Machine Learning
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)
A 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
啟用和損失函式
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 functions 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 Networks (stackoverflow.com)
https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks/2499936#2499936
Bias Nodes in Neural Networks(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
感知機
Perceptrons (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#perceptrons
The Perception (natureofcode.com)
https://natureofcode.com/book/chapter-10-neural-networks/#chapter10_figure3
Single-layer Neural Networks (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-perceptrons-to-deep-networks
迴歸
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)
Simple Linear Regression Tutorial for Machine Learning
Logistic Regression Tutorial for Machine Learning(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/
生成學習
Generative Learning Algorithms (Stanford CS229)
http://cs229.stanford.edu/notes/cs229-notes2.pdf
A practical explanation of a Naive Bayes classifier (monkeylearn.com)
A practical explanation of a Naive Bayes classifier
支援向量機
An introduction to Support Vector Machines (SVM) (monkeylearn.com)
An 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/
深度學習
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)
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/
優化和降維
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/
長短期記憶（LSTM）
A Gentle Introduction to Long Short-Term Memory Networks by the Experts(machinelearningmastery.com)
A Gentle Introduction to Long Short-Term Memory Networks by the Experts
Understanding LSTM Networks (colah.github.io)
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Exploring LSTMs (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/
卷積神經網路
Introducing convolutional networks (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap6.html#introducing_convolutional_networks
Deep Learning and Convolutional Neural Networks(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/
遞迴神經網路
Recurrent Neural Networks Tutorial (wildml.com)
Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs
Attention and Augmented Recurrent Neural Networks (distill.pub)
http://distill.pub/2016/augmented-rnns/
The Unreasonable Effectiveness of Recurrent Neural Networks(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/
強化學習
Simple Beginner’s guide to Reinforcement Learning & its implementation(analyticsvidhya.com)
Simple Beginner’s guide to Reinforcement Learning & its implementation
A Tutorial for Reinforcement Learning (mst.edu)
https://web.mst.edu/~gosavia/tutorial.pdf
Learning Reinforcement Learning (wildml.com)
Learning Reinforcement Learning (with Code, Exercises and Solutions)
Deep Reinforcement Learning: Pong from Pixels (karpathy.github.io)
http://karpathy.github.io/2016/05/31/rl/
生成對抗網路（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 TensorFlow) (aylien.com)
An introduction to Generative Adversarial Networks (with code in TensorFlow)
Generative Adversarial Networks for Beginners (oreilly.com)
https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners
多工學習
An Overview of Multi-Task Learning in Deep Neural Networks(sebastianruder.com)
http://sebastianruder.com/multi-task/index.html
自然語言處理
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)
Introduction to 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
深度學習和自然語言處理
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 Networks for NLP (wildml.com)
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 Networks 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
詞向量
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)
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/
編碼器-解碼器
Attention and Memory in Deep Learning and NLP (wildml.com)
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 Networks (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
範例
How To Implement The Perceptron Algorithm From Scratch In Python(machinelearningmastery.com)
How To Implement The Perceptron Algorithm From Scratch In Python
Implementing a Neural Network from Scratch in Python (wildml.com)
Implementing a Neural Network from Scratch in Python – An Introduction
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
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
Tensorflow
Tensorflow Tutorials (tensorflow.org)
https://www.tensorflow.org/tutorials/
Introduction to TensorFlow — CPU vs GPU (medium.com/@erikhallstrm)
https://medium.com/@erikhallstrm/hello-world-tensorflow-649b15aed18c
TensorFlow: A primer (metaflow.fr)
https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3
RNNs in Tensorflow (wildml.com)
RNNs in Tensorflow, a Practical Guide and Undocumented Features
Implementing a CNN for Text Classification in TensorFlow (wildml.com)
Implementing a CNN for Text Classification in TensorFlow
How to Run Text Summarization with TensorFlow (surmenok.com)
http://pavel.surmenok.com/2016/10/15/how-to-run-text-summarization-with-tensorflow/
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
數學
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
線性代數
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
微積分
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
相關報道：
https://medium.com/machine-learning-in-practice/over-200-of-the-best-machine-learning-nlp-and-python-tutorials-2018-edition-dd8cf53cb7dc
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