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【2018最新版】 200个机器学习 && NLP && Python 相关教程

2018 年 8 月 2 日 机器学习算法与Python学习

近年来，机器学习等新最新技术层出不穷，如何跟踪最新的热点以及最新资源，作者Robbie Allen列出了一系列相关资源教程列表，包含四个主题：机器学习，自然语言处理，Python和数学，建议大家收藏学习！

由于链接很多，为大家准备了PDF版，方便学习

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公众号后天回复“20180802”

方式二

点击文末阅读原文

作者 | Robbie Allen

编译 | 专知

整理 | Sanglei, Shengsheng

添加微信：MLAPython

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Over 200 of the Best Machine Learning, NLP, and Python Tutorials

— 2018 Edition

去年我写了一份相当受欢迎的博文(在Medium上有16万阅读量，相关资源1)，列出了我在深入研究大量机器学习资源时发现的最佳教程。十三个月后，现在有许多关于传统机器学习概念的新教程大量涌现以及过去一年中出现的新技术。围绕机器学习持续增加的大量内容有着惊人的数量。

本文包含了迄今为止我发现的最好的一些教程内容。它绝不是网上每个ML相关教程的简单详尽列表（这个工作量无疑是十分巨大而又枯燥重复的），而是经过详细筛选后的结果。我的目标就是将我在机器学习和自然语言处理领域各个方面找到的我认为最好的教程整理出来。

在教程中，为了能够更好的让读者理解其中的概念，我将避免罗列书中每章的详细内容，而是总结一些概念性的介绍内容。为什么不直接去买本书？当你想要对某些特定的主题或者不同方面进行了初步了解时，我相信这些教程对你可能帮助更大。

本文中我将分四个主题进行整理: 机器学习，自然语言处理，Python和数学。在每个主题中我将包含一个例子和多个资源。当然我不可能完全覆盖所有的主题啦。

如果你发现我在这里遗漏了好的教程资源，请联系告诉我。为了避免资源重复罗列，我在每个主题下只列出了5、6个教程。下面的每个链接都应该链接了和其他链接不同的资源，也会通过不同的方式（例如幻灯片代码段)或者不同的角度呈现出这些内容。

1. 机器学习

Start Here with MachineLearning (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: BestPractices for ML Engineering(martin.zinkevich.org)
http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

Machine Learning CrashCourse: Part I, Part II, Part III (Machine Learning atBerkeley)

Part I https://ml.berkeley.edu/blog/2016/11/06/tutorial-1/
Part II https://ml.berkeley.edu/blog/2016/12/24/tutorial-2/
Part III https://ml.berkeley.edu/blog/2017/02/04/tutorial-3/

An Introduction to MachineLearning Theory and Its Applications: A Visual Tutorial withExamples (toptal.com)
https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer

A Gentle Guide to MachineLearning (monkeylearn.com)
https://monkeylearn.com/blog/a-gentle-guide-to-machine-learning/

Which machine learningalgorithm should I use? (sas.com)
https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/

The Machine LearningPrimer (sas.com)
https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/machine-learning-primer-108796.pdf

Machine Learning Tutorial forBeginners (kaggle.com/kanncaa1)
https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners

1.1 激活函数与损失函数

Sigmoidneurons (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#sigmoid_neurons

What is the role of theactivation 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 ofactivation 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’stypes-Which is better? (medium.com)
https://medium.com/towards-data-science/activation-functions-and-its-types-which-is-better-a9a5310cc8f

Making Sense of LogarithmicLoss (exegetic.biz)
http://www.exegetic.biz/blog/2015/12/making-sense-logarithmic-loss/

Loss Functions (StanfordCS231n)
http://cs231n.github.io/neural-networks-2/#losses

L1 vs. L2 Lossfunction (rishy.github.io)
http://rishy.github.io/ml/2015/07/28/l1-vs-l2-loss/

The cross-entropy costfunction (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function

1.2 偏差(bias)

Role of Bias in NeuralNetworks (stackoverflow.com)
https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks/2499936#2499936

Bias Nodes in NeuralNetworks (makeyourownneuralnetwork.blogspot.com)
http://makeyourownneuralnetwork.blogspot.com/2016/06/bias-nodes-in-neural-networks.html

What is bias in artificialneural network? (quora.com)
https://www.quora.com/What-is-bias-in-artificial-neural-network

1.3 感知机（perceptron）

Perceptrons (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#perceptrons

The Perception (natureofcode.com)
http://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 DeepNetworks (toptal.com)
https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

1.4 回归（Regression）

Introduction to linearregression analysis (duke.edu)
http://people.duke.edu/~rnau/regintro.htm

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

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

Logistic Regression (readthedocs.io)
http://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html

Simple Linear RegressionTutorial for Machine Learning(machinelearningmastery.com)
http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/

Logistic Regression Tutorialfor Machine Learning(machinelearningmastery.com)
http://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/

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

1.5 梯度下降（Gradient Descent）

Learning with gradientdescent (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#learning_with_gradient_descent

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

How to understand GradientDescent algorithm (kdnuggets.com)
http://www.kdnuggets.com/2017/04/simple-understand-gradient-descent-algorithm.html

An overview of gradient descentoptimization algorithms(sebastianruder.com)
http://sebastianruder.com/optimizing-gradient-descent/

Optimization: StochasticGradient Descent (Stanford CS231n)
http://cs231n.github.io/optimization-1/

1.6 生成学习（Generative Learning）

Generative LearningAlgorithms (Stanford CS229)
http://cs229.stanford.edu/notes/cs229-notes2.pdf

A practical explanation of aNaive Bayes classifier (monkeylearn.com)
https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/

1.7 支持向量机（Support Vector Machines）

An introduction to SupportVector Machines (SVM) (monkeylearn.com)
https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/

Support VectorMachines (Stanford CS229)
http://cs229.stanford.edu/notes/cs229-notes3.pdf

Linear classification: SupportVector Machine, Softmax (Stanford 231n)
http://cs231n.github.io/linear-classify/

1.8 反向传播（Backpropagation）

Yes you should understandbackprop (medium.com/@karpathy)
https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b

Can you give a visualexplanation for the back propagation algorithm for neural networks? (github.com/rasbt)
https://github.com/rasbt/python-machine-learning-book/blob/master/faq/visual-backpropagation.md

How the backpropagationalgorithm works(neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap2.html

Backpropagation Through Timeand Vanishing Gradients (wildml.com)
http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/

A Gentle Introduction toBackpropagation Through Time(machinelearningmastery.com)
http://machinelearningmastery.com/gentle-introduction-backpropagation-time/

Backpropagation,Intuitions (Stanford CS231n)
http://cs231n.github.io/optimization-2/

1.9 深度学习（Deep Learning）

A Guide to Deep Learning byYN² (yerevann.com)
http://yerevann.com/a-guide-to-deep-learning/

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

Deep Learning in aNutshell (nikhilbuduma.com)
http://nikhilbuduma.com/2014/12/29/deep-learning-in-a-nutshell/

A Tutorial on DeepLearning (Quoc V. Le)
http://ai.stanford.edu/~quocle/tutorial1.pdf

What is DeepLearning? (machinelearningmastery.com)
http://machinelearningmastery.com/what-is-deep-learning/

What’s the Difference BetweenArtificial 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—TheStraight Dope (gluon.mxnet.io)
https://gluon.mxnet.io/

1.10 优化与降维（Optimization and Dimensionality Reduction）

Seven Techniques for DataDimensionality Reduction (knime.org)
https://www.knime.org/blog/seven-techniques-for-data-dimensionality-reduction

Principal componentsanalysis (Stanford CS229)
http://cs229.stanford.edu/notes/cs229-notes10.pdf

Dropout: A simple way toimprove neural networks (Hinton @ NIPS 2012)
http://videolectures.net/site/normal_dl/tag=741100/nips2012_hinton_networks_01.pdf

How to train your Deep NeuralNetwork (rishy.github.io)
http://rishy.github.io/ml/2017/01/05/how-to-train-your-dnn/

1.11 Long Short Term Memory (LSTM)

A Gentle Introduction to LongShort-Term Memory Networks by the Experts(machinelearningmastery.com)
http://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/

Understanding LSTMNetworks (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 anLSTM-RNN in Python (iamtrask.github.io)
http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

1.12 卷积神经网络 Convolutional Neural Networks (CNNs)

Introducing convolutionalnetworks (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap6.html#introducing_convolutional_networks

Deep Learning and ConvolutionalNeural Networks(medium.com/@ageitgey)
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721

Conv Nets: A ModularPerspective (colah.github.io)
http://colah.github.io/posts/2014-07-Conv-Nets-Modular/

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

1.13 循环神经网络 Recurrent Neural Nets (RNNs)

Recurrent Neural NetworksTutorial (wildml.com)
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/

Attention and AugmentedRecurrent Neural Networks (distill.pub)
http://distill.pub/2016/augmented-rnns/

The Unreasonable Effectivenessof Recurrent Neural Networks(karpathy.github.io)
http://karpathy.github.io/2015/05/21/rnn-effectiveness/

A Deep Dive into RecurrentNeural Nets (nikhilbuduma.com)
http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/

1.14 强化学习 Reinforcement Learning

Simple Beginner’s guide toReinforcement Learning & its implementation(analyticsvidhya.com)
https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/

A Tutorial for ReinforcementLearning (mst.edu)
https://web.mst.edu/~gosavia/tutorial.pdf

Learning ReinforcementLearning (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/

1.15 生产对抗模型 Generative Adversarial Networks (GANs)

Adversarial MachineLearning (aaai18adversarial.github.io)
https://aaai18adversarial.github.io/slides/AML.pptx

What’s a Generative AdversarialNetwork? (nvidia.com)
https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network/

Abusing Generative AdversarialNetworks 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 GenerativeAdversarial Networks (with code in TensorFlow) (aylien.com)
http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/

Generative Adversarial Networksfor Beginners (oreilly.com)
https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners

1.16 多任务学习 Multi-task Learning

An Overview of Multi-TaskLearning in Deep Neural Networks(sebastianruder.com)
http://sebastianruder.com/multi-task/index.html

2. 自然语言处理 NLP

Natural Language Processing isFun! (medium.com/@ageitgey)
https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e

A Primer on Neural NetworkModels for Natural LanguageProcessing (Yoav Goldberg)
http://u.cs.biu.ac.il/~yogo/nnlp.pdf

The Definitive Guide to NaturalLanguage Processing (monkeylearn.com)
https://monkeylearn.com/blog/the-definitive-guide-to-natural-language-processing/

Introduction to NaturalLanguage 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

2.1 深度学习与自然语言处理 Deep Learning and NLP

Deep Learning applied toNLP (arxiv.org)
https://arxiv.org/pdf/1703.03091.pdf

Deep Learning for NLP (withoutMagic) (Richard Socher)
https://nlp.stanford.edu/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdf

Understanding ConvolutionalNeural Networks for NLP (wildml.com)
http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/

Deep Learning, NLP, andRepresentations (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 NLPmodels (explosion.ai)
https://explosion.ai/blog/deep-learning-formula-nlp

Understanding Natural Languagewith Deep Neural Networks Using Torch(nvidia.com)
https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/

Deep Learning for NLP withPytorch (pytorich.org)
http://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html

2.2 词向量 Word Vectors

Bag of Words Meets Bags ofPopcorn (kaggle.com)
https://www.kaggle.com/c/word2vec-nlp-tutorial

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

Part I :http://sebastianruder.com/word-embeddings-1/index.html
Part II: http://sebastianruder.com/word-embeddings-softmax/index.html
Part III: http://sebastianruder.com/secret-word2vec/index.html

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

word2vec Parameter LearningExplained (arxiv.org)
https://arxiv.org/pdf/1411.2738.pdf

Word2Vec Tutorial—TheSkip-Gram Model, Negative Sampling(mccormickml.com)
http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/

2.3 编解码模型 Encoder-Decoder

Attention and Memory in DeepLearning and NLP (wildml.com)
http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/

Sequence to SequenceModels (tensorflow.org)
https://www.tensorflow.org/tutorials/seq2seq

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

How to use an Encoder-DecoderLSTM to Echo Sequences of Random Integers(machinelearningmastery.com)
http://machinelearningmastery.com/how-to-use-an-encoder-decoder-lstm-to-echo-sequences-of-random-integers/

tf-seq2seq (google.github.io)
https://google.github.io/seq2seq/

3. Python

Machine Learning CrashCourse (google.com)
https://developers.google.com/machine-learning/crash-course/

Awesome MachineLearning (github.com/josephmisiti)
https://github.com/josephmisiti/awesome-machine-learning#python

7 Steps to Mastering MachineLearning With Python (kdnuggets.com)
http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html

An example machine learningnotebook (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 withPython (tutorialspoint.com)
https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_quick_guide.htm

3.1 样例 Examples

How To Implement The PerceptronAlgorithm From Scratch In Python(machinelearningmastery.com)
http://machinelearningmastery.com/implement-perceptron-algorithm-scratch-python/

Implementing a Neural Networkfrom Scratch in Python (wildml.com)
http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/

A Neural Network in 11 lines ofPython (iamtrask.github.io)
http://iamtrask.github.io/2015/07/12/basic-python-network/

Implementing Your Own k-NearestNeighbour Algorithm Using Python(kdnuggets.com)
http://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html

ML fromScatch (github.com/eriklindernoren)
https://github.com/eriklindernoren/ML-From-Scratch

Python Machine Learning (2ndEd.) Code Repository (github.com/rasbt)
https://github.com/rasbt/python-machine-learning-book-2nd-edition

3.2 Scipy and numpy教程

Scipy LectureNotes (scipy-lectures.org)
http://www.scipy-lectures.org/

Python NumpyTutorial (Stanford CS231n)
http://cs231n.github.io/python-numpy-tutorial/

An introduction to Numpy andScipy (UCSB CHE210D)
https://engineering.ucsb.edu/~shell/che210d/numpy.pdf

A Crash Course in Python forScientists (nbviewer.jupyter.org)
http://nbviewer.jupyter.org/gist/rpmuller/5920182#ii.-numpy-and-scipy

3.3 scikit-learn教程

PyCon scikit-learn TutorialIndex (nbviewer.jupyter.org)
http://nbviewer.jupyter.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb

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

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

Abridged scikit-learnTutorials (github.com/mmmayo13)
https://github.com/mmmayo13/scikit-learn-beginners-tutorials

3.4 Tensorflow教程

Tensorflow Tutorials (tensorflow.org)
https://www.tensorflow.org/tutorials/

Introduction to TensorFlow—CPUvs GPU (medium.com/@erikhallstrm)
https://medium.com/@erikhallstrm/hello-world-tensorflow-649b15aed18c

TensorFlow: Aprimer (metaflow.fr)
https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3

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

Implementing a CNN for TextClassification in TensorFlow (wildml.com)
http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/

How to Run Text Summarizationwith TensorFlow (surmenok.com)
http://pavel.surmenok.com/2016/10/15/how-to-run-text-summarization-with-tensorflow/

3.5 PyTorch教程

PyTorchTutorials (pytorch.org)
http://pytorch.org/tutorials/

A Gentle Intro toPyTorch (gaurav.im)
http://blog.gaurav.im/2017/04/24/a-gentle-intro-to-pytorch/

Tutorial: Deep Learning inPyTorch (iamtrask.github.io)
https://iamtrask.github.io/2017/01/15/pytorch-tutorial/

PyTorch Examples (github.com/jcjohnson)
https://github.com/jcjohnson/pytorch-examples

PyTorchTutorial (github.com/MorvanZhou)
https://github.com/MorvanZhou/PyTorch-Tutorial

PyTorch Tutorial for DeepLearning Researchers (github.com/yunjey)
https://github.com/yunjey/pytorch-tutorial

4. 数学基础教程

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

Math for MachineLearning (UMIACS CMSC422)
http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf

4.1 线性代数

An Intuitive Guide to LinearAlgebra (betterexplained.com)
https://betterexplained.com/articles/linear-algebra-guide/

A Programmer’s Intuition forMatrix Multiplication (betterexplained.com)
https://betterexplained.com/articles/matrix-multiplication/

Understanding the Cross Product (betterexplained.com)
https://betterexplained.com/articles/cross-product/

Understanding the DotProduct (betterexplained.com)
https://betterexplained.com/articles/vector-calculus-understanding-the-dot-product/

Linear Algebra for MachineLearning (U. of Buffalo CSE574)
http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/LinearAlgebra.pdf

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

Linear Algebra Review andReference (Stanford CS229)
http://cs229.stanford.edu/section/cs229-linalg.pdf

4.2 概率论

Understanding Bayes TheoremWith Ratios (betterexplained.com)
https://betterexplained.com/articles/understanding-bayes-theorem-with-ratios/

Review of ProbabilityTheory (Stanford CS229)
http://cs229.stanford.edu/section/cs229-prob.pdf

Probability Theory Review forMachine Learning (Stanford CS229)
https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf

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

Probability Theory for MachineLearning (U. of Toronto CSC411)
http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial1.pdf

4.3 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/