● 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
● 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/
● 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
● 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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