200個精選ML、NLP、Python及數學最佳教程

知識 08-09

來源：專知

本文多資源，建議閱讀收藏。

本文列出了一系列包含四個主題的相關資源教程列表，一起來充電學習吧~

[導讀 ]近年來，機器學習等新最新技術層出不窮，如何跟蹤最新的熱點以及最新資源，作者Robbie Allen列出了一系列相關資源教程列表，包含四個主題：機器學習，自然語言處理，Python和數學，建議大家收藏學習！

去年我寫了一份相當受歡迎的博文(在Medium上有16萬閱讀量，相關資源1)，列出了我在深入研究大量機器學習資源時發現的最佳教程。十三個月後，現在有許多關於傳統機器學習概念的新教程大量湧現以及過去一年中出現的新技術。圍繞機器學習持續增加的大量內容有著驚人的數量。

本文包含了迄今為止我發現的最好的一些教程內容。它絕不是網上每個ML相關教程的簡單詳盡列表（這個工作量無疑是十分巨大而又枯燥重複的），而是經過詳細篩選後的結果。我的目標就是將我在機器學習和自然語言處理領域各個方面找到的我認為最好的教程整理出來。

在教程中，為了能夠更好的讓讀者理解其中的概念，我將避免羅列書中每章的詳細內容，而是總結一些概念性的介紹內容。為什麼不直接去買本書？當你想要對某些特定的主題或者不同方面進行了初步了解時，我相信這些教程對你可能幫助更大。

本文中我將分四個主題進行整理: 機器學習，自然語言處理，Python和數學。在每個主題中我將包含一個例子和多個資源。當然我不可能完全覆蓋所有的主題啦。

如果你發現我在這裡遺漏了好的教程資源，請聯繫告訴我。為了避免資源重複羅列，我在每個主題下只列出了5、6個教程。下面的每個鏈接都應該鏈接了和其他鏈接不同的資源，也會通過不同的方式（例如幻燈片代碼段)或者不同的角度呈現出這些內容。

一、機器學習

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 激活函數與損失函數

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 感知機（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 回歸（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)

//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 生成學習（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 支持向量機（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 反向傳播（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 深度學習（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 優化與降維（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)

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

1.12 卷積神經網路 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 循環神經網路 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 強化學習 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 生產對抗模型 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 多任務學習 Multi-task Learning

An Overview of Multi-TaskLearning in Deep Neural Networks (sebastianruder.com)

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

二、自然語言處理 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 深度學習與自然語言處理 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 詞向量 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 編解碼模型 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/

三、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 樣例 Examples

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 ofPython (iamtrask.github.io)

//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-learn Tutorials (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

四、數學基礎教程

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 線性代數

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 概率論

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: Understandingthe Gradient (betterexplained.com)

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

DifferentialCalculus (Stanford CS224n)

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

CalculusOverview (readthedocs.io)

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

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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