超級大彙總！200多個最好的機器學習、NLP和Python教程

這篇文章包含了我目前爲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。我這裏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接到一起。

我這裏指的“教程”，是指那些爲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裏的章節，因爲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，爲何不直接買書呢?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。

我把這篇文章分爲了四個部分：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，但是因爲材料的數量龐大，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。每一個鏈接都包含了與其他鏈接不同的材料，或使用了不同的方式表達信息(例如：使用代碼，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)

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

激活和損失函數

• 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)

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)

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

支持向量機

• 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)

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/

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)

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-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/

卷積神經網絡

• 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/

遞歸神經網絡

• Recurrent Neural Networks Tutorial (wildml.com)

http://www.wildml.com/2015/09/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)

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/

生成對抗網絡(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)

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

多任務學習

• 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)

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)

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 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)

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/

編碼器-解碼器

• 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 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)

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

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)

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

• Implementing a CNN for Text Classification in TensorFlow (wildml.com)

http://www.wildml.com/2015/12/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

• 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

原文發佈時間爲：2018-10-28c

本文來自雲棲社區合作伙伴“大數據地盤”，瞭解相關信息可以關注“大數據地盤”。