【导读】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！
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。
相关资源
作者Robbie Allen是以为科技作者和创业者、并自学AI并成为博士生。曾整理许多广为流传的机器学习相关资源。
1. 2017版教程资源 Over 150 ofthe Best Machine Learning, NLP, and Python Tutorials I’ve Found（150多个最好的与机器学习,自然语言处理和Python相关的教程）
英文：
https://medium.com/machine-learning-in-practice/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78
中文翻译：http://pytlab.org
2. My Curated List of AI and Machine LearningResources from Around the Web( 终极收藏AI领域你不能不关注的大牛、机构、课程、会议、图书)
英文：
https://medium.com/machine-learning-in-practice/my-curated-list-of-ai-and-machine-learning-resources-from-around-the-web-9a97823b8524
中文翻译：
http://www.sohu.com/a/168291972_473283
3. Cheat Sheet of Machine Learningand Python (and Math) Cheat Sheets
（值得收藏的27 个机器学习的小抄）
英文：
https://medium.com/machine-learning-in-practice/cheat-sheet-of-machine-learning-and-python-and-math-cheat-sheets-a4afe4e791b6
目录
1.机器学习
1.1 激活函数与损失函数
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 生产对抗模型 Generative Adversarial Networks (GANs)
1.16 多任务学习 Multi-task Learning
2. 自然语言处理 NLP
2.1 深度学习与自然语言处理 Deep Learning and NLP
2.2 词向量 Word Vectors
2.3 编解码模型 Encoder-Decoder
3. 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
4. 数学基础教程
4.1 线性代数
4.2 概率论
4.3 微积分
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/
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
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