本文多资源，建议阅读收藏。
本文列出了一系列包含四个主题的相关资源教程列表。让我们充电学习~
[ 导读 ]近年来，机器学习等新技术层出不穷。作者如何跟踪最新的热点和资源？Robbie Allen列出了机器学习、自然语言处理等一系列相关资源教程，Python还有数学，建议大家收藏学习！
去年我写了一篇很受欢迎的博文(在Medium阅读量16万，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，出现了大量关于传统机器学习概念的新教程和去年出现的新技术。围绕机器学习不断增加的大量内容数量惊人。
本文包含了我迄今为止发现的最好的教程内容。它绝不是互联网上的每一个ML相关教程的简单详细列表(这个工作量无疑是巨大而枯燥的重复)，而是经过详细筛选的结果。我的目标是整理出我在机器学习和自然语言处理领域找到的最好的教程。
在教程中，为了更好地让读者理解概念，我将避免列出书中每章的详细内容，而是总结一些概念介绍。为什么不直接买这本书呢？当你想对某些特定的主题或不同的方面有一个初步的了解时，我相信这些教程可能会对你更有帮助。
本文将分为四个主题： 机器学习，自然语言处理，Python数学。我将在每个主题中包含一个例子和多个资源。当然，我不能完全涵盖所有的主题。
如果你发现我错过了这里的好教程资源，请联系我。为了避免重复列出资源，我只在每个主题下列出了5或6个教程。以下链接应链接不同于其他链接的资源，并以不同的方式（如幻灯片代码段）或不同的角度呈现。
相关资源
作者Robbie Allen认为科技作家和企业家自学AI并成为博士生。组织了许多流行的机器学习资源。
1. 2022版教程资源 Over 150 ofthe Best Machine Learning, NLP, and Python Tutorials I’ve Found(机器学习、自然语言处理和Python相关教程)
英文：medium/machine-learning-in-practice/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78中文翻译：pytlab
2. My Curated List of AI and Machine LearningResources from Around the Web( 终极收藏AI大牛、机构、课程、会议、书籍等你不能关注的领域)
英文：medium/machine-learning-in-practice/my-curated-list-of-ai-and-machine-learning-resources-from-around-the-web-9a97823b8524中文翻译：sohu/a/168291972_473283
3. Cheat Sheet of Machine Learningand Python (and Math) Cheat Sheets(27值得收藏 机器学习的小抄)
英文：medium/machine-learning-in-practice/cheat-sheet-of-machine-learning-and-python-and-math-cheat-sheets-a4afe4e791b6
目录
一、机器学习
1.1 激活函数和损失函数
1.2 偏差(bias)
1.3 感知机（perceptron）
1.4 回归（Regression）
1.5 梯度下降（Gradient Descent）
1.6 生成学习（Generative Learning）
1.7 支持向量机（Support Vector Machines）
1.8 反向传播（Backpropagation）
1.9 深度学习（Deep Learning）
1.10 优化与降维（Optimization and Dimensionality Reduction）
1.11 Long Short Term Memory (LSTM)
1.12 卷积神经网络 Convolutional Neural Networks (CNNs)
1.13 循环神经网络 Recurrent Neural Nets (RNNs)
1.14 强化学习 Reinforcement Learning
1.15 对抗模型的生产 Generative Adversarial Networks (GANs)
1.16 多任务学习 Multi-task Learning
二、自然语言处理 NLP
2.1 深度学习和自然语言处理 Deep Learning and NLP
2.2 词向量 Word Vectors
2.3 编解码模型 Encoder-Decoder
三、Python
3.1 样例 Examples
3.2 Scipy and numpy教程
3.3 scikit-learn教程
3.4 Tensorflow教程
3.5 PyTorch教程
四、数学基础教程
4.1 线性代数
4.2 概率论
4.3 微积分
一、机器学习Start Here with MachineLearning (machinelearningmastery)machinelearningmastery/start-here/Machine Learning is Fun! (medium/@ageitgey)medium/@ageitgey/machine-learning-is-fun-80ea3ec3c471Rules of Machine Learning: BestPractices for ML Engineering(martin.zinkevich)martin.zinkevich/rules_of_ml/rules_of_ml.pdfMachine Learning CrashCourse: Part I, Part II, Part III (Machine Learning atBerkeley)Part Iml.berkeley/blog/2022/11/06/tutorial-1/Part II ml.berkeley/blog/2022/12/24/tutorial-2/Part III ml.berkeley/blog/2022/02/04/tutorial-3/An Introduction to MachineLearning Theory and Its Applications: A Visual Tutorial withExamples (toptal)toptal/machine-learning/machine-learning-theory-an-introductory-primerA Gentle Guide to MachineLearning (monkeylearn)monkeylearn/blog/a-gentle-guide-to-machine-learning/Which machine learningalgorithm should I use? (sas)blogs.sas/content/subconsciousmusings/2022/04/12/machine-learning-algorithm-use/The Machine LearningPrimer (sas)sas/content/dam/SAS/en_us/doc/whitepaper1/machine-learning-primer-108796.pdfMachine Learning Tutorial forBeginners (kaggle/kanncaa1)kaggle/kanncaa1/machine-learning-tutorial-for-beginners
1.1 激活函数和损失函数
Sigmoidneurons (neuralnetworksanddeeplearning)neuralnetworksanddeeplearning/chap1.html#sigmoid_neuronsWhat is the role of theactivation function in a neural network? (quora)quora/What-is-the-role-of-the-activation-function-in-a-neural-networkComprehensive list ofactivation functions in neural networks with pros/cons(stats.stackexchange)stats.stackexchange/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-consActivation functions and it’stypes-Which is better? (medium)medium/towards-data-science/activation-functions-and-its-types-which-is-better-a9a5310cc8fMaking Sene of LogarithmicLoss (exegetic)exegetic/blog/2022/12/making-sense-logarithmic-loss/Loss Functions (StanfordCS231n)cs231n.github/neural-networks-2/#lossesL1 vs. L2 Lossfunction (rishy.github.io)rishy.github/ml/2022/07/28/l1-vs-l2-loss/The cross-entropy costfunction (neuralnetworksanddeeplearning)neuralnetworksanddeeplearning/chap3.html#the_cross-entropy_cost_function
1.2 偏差(bias)
Role of Bias in NeuralNetworks (stackoverflow)stackoverflow/questions/2480650/role-of-bias-in-neural-networks/2499936#2499936Bias Nodes in NeuralNetworks (makeyourownneuralnetwork.blogspot)makeyourownneuralnetwork.blogspot/2022/06/bias-nodes-in-neural-networks.htmlWhat is bias in artificialneural network? (quora)quora/What-is-bias-in-artificial-neural-network
1.3 感知机（perceptron）
Perceptrons (neuralnetworksanddeeplearning)neuralnetworksanddeeplearning/chap1.html#perceptronsThe Perception (natureofcode)natureofcode/book/chapter-10-neural-networks/#chapter10_figure3Single-layer Neural Networks (Perceptrons) (dcu.ie)computing.dcu.ie/~humphrys/Notes/Neural/single.neural.htmlFrom Perceptrons to DeepNetworks (toptal)toptal/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks
1.4 回归（Regression）
Introduction to linearregression analysis (duke.edu)people.duke/~rnau/regintro.htmLinearRegression (ufldl.stanford.edu)ufldl.stanford/tutorial/supervised/LinearRegression/LinearRegression (readthedocs.io)ml-cheatsheet.readthedocs/en/latest/linear_regression.htmlLogistic Regression (readthedocs.io)ml-cheatsheet.readthedocs/en/latest/logistic_regression.htmlSimple Linear RegressionTutorial for Machine Learning (machinelearningmastery)machinelearningmastery/simple-linear-regression-tutorial-for-machine-learning/Logistic Regression Tutorialfor Machine Learning(machinelearningmastery)machinelearningmastery/logistic-regression-tutorial-for-machine-learning/SoftmaxRegression (ufldl.stanford.edu)ufldl.stanford/tutorial/supervised/SoftmaxRegression/
1.5 梯度下降（Gradient Descent）
Learning with gradientdescent (neuralnetworksanddeeplearning)neuralnetworksanddeeplearning/chap1.html#learning_with_gradient_descentGradientDescent (iamtrask.github.io)iamtrask.github/2022/07/27/python-network-part2/How to understand GradientDescent algorithm (kdnuggets)kdnuggets/2022/04/simple-understand-gradient-descent-algorithm.htmlAn overview of gradient descentoptimization algorithms (sebastianruder)sebastianruder/optimizing-gradient-descent/Optimization: StochasticGradient Descent (Stanford CS231n)cs231n.github/optimization-1/
1.6 生成学习（Generative Learning）
Generative LearningAlgorithms (Stanford CS229)cs229.stanford/notes/cs229-notes2.pdfA practical explanation of aNaive Bayes classifier (monkeylearn)monkeylearn/blog/practical-explanation-naive-bayes-classifier/
1.7 支持向量机（Support Vector Machines）
An introduction to SupportVector Machines (SVM) (monkeylearn)monkeylearn/blog/introduction-to-support-vector-machines-svm/Support VectorMachines (Stanford CS229)cs229.stanford/notes/cs229-notes3.pdfLinear classification: SupportVector Machine, Softmax (Stanford 231n)cs231n.github/linear-classify/
1.8 反向传播（Backpropagation）
Yes you should understandbackprop (medium/@karpathy)medium/@karpathy/yes-you-should-understand-backprop-e2f06eab496bCan you give a visualexplanation for the back propagation algorithm for neural networks? (github/rasbt)github/rasbt/python-machine-learning-book/blob/master/faq/visual-backpropagation.mdHow the backpropagationalgorithm works(neuralnetworksanddeeplearning)neuralnetworksanddeeplearning/chap2.htmlBackpropagation Through Timeand Vanishing Gradients (wildml)wildml/2022/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/A Gentle Introduction toBackpropagation Through Time(machinelearningmastery)machinelearningmastery/gentle-introduction-backpropagation-time/Backpropagation,Intuitions (Stanford CS231n)cs231n.github/optimization-2/
1.9 深度学习（Deep Learning）
A Guide to Deep Learning byYN2 (yerevann)yerevann/a-guide-to-deep-learning/Deep Learning Papers ReadingRoadmap (github/floodsung)github/floodsung/Deep-Learning-Papers-Reading-RoadmapDeep Learning in aNutshell (nikhilbuduma)nikhilbuduma/2014/12/29/deep-learning-in-a-nutshell/A Tutorial on DeepLearning (Quoc V. Le)ai.stanford/~quocle/tutorial1.pdfWhat is DeepLearning? (machinelearningmastery)machinelearningmastery/what-is-deep-learning/What’s the Difference BetweenArtificial Intelligence, Machine Learning, and Deep Learning? (nvidia)blogs.nvidia/blog/2022/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/Deep Learning—TheStraight Dope (gluon.mxnet.io)gluon.mxnet/
1.10 优化与降维（Optimization and Dimensionality Reduction）
Seven Techniques for DataDimensionality Reduction (knime)knime/blog/seven-techniques-for-data-dimensionality-reductionPrincipal componentsanalysis (Stanford CS229)cs229.stanford/notes/cs229-notes10.pdfDropout: A simple way toimprove neural networks (Hinton @ NIPS 2012)videolectures/site/normal_dl/tag=741100/nips2012_hinton_networks_01.pdfHow to train your Deep NeuralNetwork (rishy.github.io)rishy.github/ml/2022/01/05/how-to-train-your-dnn/
1.11 Long Short Term Memory (LSTM)
A Gentle Introduction to LongShort-Term Memory Networks by the Experts(machinelearningmastery)machinelearningmastery/gentle-introduction-long-short-term-memory-networks-experts/Understanding LSTMNetworks (colah.github.io)colah.github/posts/2022-08-Understanding-LSTMs/Exploring LSTMs (echen)blog.echen/2022/05/30/exploring-lstms/Anyone Can Learn To Code anLSTM-RNN in Python (iamtrask.github.io)iamtrask.github/2022/11/15/anyone-can-code-lstm/
1.12 卷积神经网络 Convolutional Neural Networks (CNNs)
Introducing convolutionalnetworks (neuralnetworksanddeeplearning)neuralnetworksanddeeplearning/chap6.html#introducing_convolutional_networksDeep Learning and ConvolutionalNeural Networks(medium/@ageitgey)medium/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721Conv Nets: A ModularPerspective (colah.github.io)colah.github/posts/2014-07-Conv-Nets-Modular/UnderstandingConvolutions (colah.github.io)colah.github/posts/2014-07-Understanding-Convolutions/
1.13 循环神经网络 Recurrent Neural Nets (RNNs)
Recurrent Neural NetworksTutorial (wildml)wildml/2022/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/Attention and AugmentedRecurrent Neural Networks (distill)distill/2022/augmented-rnns/The Unreasonable Effectivenessof Recurrent Neural Networks (karpathy.github.io)karpathy.github/2022/05/21/rnn-effectiveness/A Deep Dive into RecurrentNeural Nets (nikhilbuduma)nikhilbuduma/2022/01/11/a-deep-dive-into-recurrent-neural-networks/
1.14 强化学习 Reinforcement Learning
Simple Beginner’s guide toReinforcement Learning & its implementation(analyticsvidhya)analyticsvidhya/blog/2022/01/introduction-to-reinforcement-learning-implementation/A Tutorial for ReinforcementLearning (mst.edu)web.mst/~gosavia/tutorial.pdfLearning ReinforcementLearning (wildml)wildml/2022/10/learning-reinforcement-learning/Deep Reinforcement Learning:Pong from Pixels (karpathy.github.io)karpathy.github/2022/05/31/rl/
1.15 生产对抗模型 Generative Adversarial Networks (GANs)
Adversarial MachineLearning (aaai18adversarial.github.io)aaai18adversarial.github/slides/AML.pptxWhat’s a Generative AdversarialNetwork? (nvidia)blogs.nvidia/blog/2022/05/17/generative-adversarial-network/Abusing Generative AdversarialNetworks to Make 8-bit Pixel Art(medium/@ageitgey)medium/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7An introduction to GenerativeAdversarial Networks (with code in TensorFlow) (aylien)blog.aylien/introduction-generative-adversarial-networks-code-tensorflow/Generative Adversarial Networksfor Beginners (oreilly)oreilly/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)sebastianruder/multi-task/index.html二、自然语言处理 NLPNatural Language Processing isFun! (medium/@ageitgey)medium/@ageitgey/natural-language-processing-is-fun-9a0bff37854eA Primer on Neural NetworkModels for Natural LanguageProcessing (Yoav Goldberg)u.cs.biu.ac.il/~yogo/nnlp.pdfThe Definitive Guide to NaturalLanguage Processing (monkeylearn)monkeylearn/blog/the-definitive-guide-to-natural-language-processing/Introduction to NaturalLanguage Processing (algorithmia)blog.algorithmia/introduction-natural-language-processing-nlp/Natural Language Processing Tutorial (vikparuchuri)vikparuchuri/blog/natural-language-processing-tutorial/Natural Language Processing(almost) from Scratch (arxiv)arxiv/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)arxiv/pdf/1703.03091.pdfDeep Learning for NLP (withoutMagic) (Richard Socher)nlp.stanford/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdfUnderstanding ConvolutionalNeural Networks for NLP (wildml)wildml/2022/11/understanding-convolutional-neural-networks-for-nlp/Deep Learning, NLP, andRepresentations (colah.github.io)colah.github/posts/2014-07-NLP-RNNs-Representations/Embed, encode, attend, predict:The new deep learning formula for state-of-the-art NLPmodels (explosion.ai)explosion.ai/blog/deep-learning-formula-nlpUnderstanding Natural Languagewith Deep Neural Networks Using Torch (nvidia)devblogs.nvidia/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/Deep Learning for NLP withPytorch (pytorich)pytorch/tutorials/beginner/deep_learning_nlp_tutorial.html
2.2 词向量 Word Vectors
Bag of Words Meets Bags ofPopcorn (kaggle)kaggle/c/word2vec-nlp-tutorialOn word embeddings PartI, Part II, Part III (sebastianruder)Part I :sebastianruder/word-embeddings-1/index.htmlPart II: sebastianruder/word-embeddings-softmax/index.htmlPart III: sebastianruder/secret-word2vec/index.htmlThe amazing power of wordvectors (acolyer)blog.acolyer/2022/04/21/the-amazing-power-of-word-vectors/word2vec Parameter LearningExplained (arxiv)arxiv/pdf/1411.2738.pdfWord2Vec Tutorial—TheSkip-Gram Model, Negative Sampling (mccormickml)mccormickml/2022/04/19/word2vec-tutorial-the-skip-gram-model/
2.3 编解码模型 Encoder-Decoder
Attention and Memory in DeepLearning and NLP (wildml)wildml/2022/01/attention-and-memory-in-deep-learning-and-nlp/Sequence to SequenceModels (tensorflow)tensorflow/tutorials/seq2seqSequence to Sequence Learningwith Neural Networks (NIPS 2014)papers.nips/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdfMachine Learning is Fun Part 5:Language Translation with Deep Learning and the Magic ofSequences (medium/@ageitgey)medium/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aaHow to use an Encoder-DecoderLSTM to Echo Sequences of Random Integers(machinelearningmastery)machinelearningmastery/how-to-use-an-encoder-decoder-lstm-to-echo-sequences-of-random-integers/tf-seq2seq (google.github.io)google.github/seq2seq/三、PythonMachine Learning CrashCourse (google)developers.google/machine-learning/crash-course/Awesome MachineLearning (github/josephmisiti)github/josephmisiti/awesome-machine-learning#python7 Steps to Mastering MachineLearning With Python (kdnuggets)kdnuggets/2022/11/seven-steps-machine-learning-python.htmlAn example machine learningnotebook (nbviewer.jupyter)nbviewer.jupyter/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynbMachine Learning withPython (tutorialspoint)tutorialspoint/machine_learning_with_python/machine_learning_with_python_quick_guide.htm
3.1 样例 Examples
How To Implement The Perceptron Algorithm From Scratch In Python(machinelearningmastery)machinelearningmastery/implement-perceptron-algorithm-scratch-python/Implementing a Neural Network from Scratch in Python (wildml)wildml/2022/09/implementing-a-neural-network-from-scratch/A Neural Network in 11 lines ofPython (iamtrask.github.io)iamtrask.github/2022/07/12/basic-python-network/Implementing Your Own k-NearestNeighbour Algorithm Using Python(kdnuggets)kdnuggets/2022/01/implementing-your-own-knn-using-python.htmlML fromScatch (github/eriklindernoren)github/eriklindernoren/ML-From-ScratchPython Machine Learning (2ndEd.) Code Repository (github/rasbt)github/rasbt/python-machine-learning-book-2nd-edition
3.2 Scipy and numpy教程
Scipy LectureNotes (scipy-lectures)scipy-lectures/Python NumpyTutorial (Stanford CS231n)cs231n.github/python-numpy-tutorial/An introduction to Numpy andScipy (UCSB CHE210D)engineering.ucsb/~shell/che210d/numpy.pdfA Crash Course in Python forScientists (nbviewer.jupyter)nbviewer.jupyter/gist/rpmuller/5920222#ii.-numpy-and-scipy
3.3 scikit-learn教程
PyCon scikit-learn TutorialIndex (nbviewer.jupyter)nbviewer.jupyter/github/jakevdp/sklearn_pycon2022/blob/master/notebooks/Index.ipynbscikit-learn ClassificationAlgorithms (github/mmmayo13)github/mmmayo13/scikit-learn-classifiers/blob/master/sklearn-classifiers-tutorial.ipynbscikit-learnTutorials (scikit-learn)scikit-learn/stable/tutorial/index.htmlAbridged scikit-learn Tutorials (github/mmmayo13)github/mmmayo13/scikit-learn-beginners-tutorials
3.4 Tensorflow教程
Tensorflow Tutorials (tensorflow)tensorflow/tutorials/Introduction to TensorFlow—CPUvs GPU (medium/@erikhallstrm)medium/@erikhallstrm/hello-world-tensorflow-649b15aed18cTensorFlow: Aprimer (metaflow.fr)blogtaflow.fr/tensorflow-a-primer-4b3fa0978be3RNNs inTensorflow (wildml)wildml/2022/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/Implementing a CNN for TextClassification in TensorFlow (wildml)wildml/2022/12/implementing-a-cnn-for-text-classification-in-tensorflow/How to Run Text Summarizationwith TensorFlow (surmenok)pavel.surmenok/2022/10/15/how-to-run-text-summarization-with-tensorflow/
3.5 PyTorch教程
PyTorchTutorials (pytorch)pytorch/tutorials/A Gentle Intro toPyTorch (gaurav)blog.gaurav/2022/04/24/a-gentle-intro-to-pytorch/Tutorial: Deep Learning inPyTorch (iamtrask.github.io)iamtrask.github/2022/01/15/pytorch-tutorial/PyTorch Examples (github/jcjohnson)github/jcjohnson/pytorch-examplesPyTorchTutorial (github/MorvanZhou)github/MorvanZhou/PyTorch-TutorialPyTorch Tutorial for DeepLearning Researchers (github/yunjey)github/yunjey/pytorch-tutorial
四、数学基础教程
Math for MachineLearning (ucsc.edu)people.ucsc/~praman1/static/pub/math-for-ml.pdfMath for MachineLearning (UMIACS CMSC422)umiacs.umd/~hal/courses/2013S_ML/math4ml.pdf
4.1 线性代数
An Intuitive Guide to LinearAlgebra (betterexplained)betterexplained/articles/linear-algebra-guide/A Programmer’s Intuition forMatrix Multiplication (betterexplained)betterexplained/articles/matrix-multiplication/Understanding the Cross Product (betterexplained)betterexplained/articles/cross-product/Understanding the DotProduct (betterexplained)betterexplained/articles/vector-calculus-understanding-the-dot-product/Linear Algebra for MachineLearning (U. of Buffalo CSE574)cedar.buffalo/~srihari/CSE574/Chap1/LinearAlgebra.pdfLinear algebra cheat sheet fordeep learning (medium)medium/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526cLinear Algebra Review andReference (Stanford CS229)cs229.stanford/section/cs229-linalg.pdf
4.2 概率论
Understanding Bayes TheoremWith Ratios (betterexplained)betterexplained/articles/understanding-bayes-theorem-with-ratios/Review of ProbabilityTheory (Stanford CS229)cs229.stanford/section/cs229-prob.pdfProbability Theory Review forMachine Learning (Stanford CS229)see.stanford/materials/aimlcs229/cs229-prob.pdfProbability Theory (U. ofBuffalo CSE574)cedar.buffalo/~srihari/CSE574/Chap1/Probability-Theory.pdfProbability Theory for MachineLearning (U. of Toronto CSC411)cs.toronto/~urtasun/courses/CSC411_Fall16/tutorial1.pdf
4.3 微积分
How To Understand Derivatives:The Quotient Rule, Exponents, and Logarithms (betterexplained)betterexplained/articles/how-to-understand-derivatives-the-quotient-rule-exponents-and-logarithms/How To Understand Derivatives:The Product, Power & Chain Rules(betterexplained)betterexplained/articles/derivatives-product-power-chain/Vector Calculus: Understandingthe Gradient (betterexplained)betterexplained/articles/vector-calculus-understanding-the-gradient/DifferentialCalculus (Stanford CS224n)web.stanford/class/cs224n/lecture_notes/cs224n-2022-review-differential-calculus.pdfCalculusOverview (readthedocs.io)ml-cheatsheet.readthedocs/en/latest/calculus.html