Start Here with Machine Learning (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: Best Practices 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 Machine Learning Theory and Its Applications: A Visual Tutorial with Examples (toptal.com)
https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer
A Gentle Guide to Machine Learning (monkeylearn.com)
https://monkeylearn.com/blog/a-gentle-guide-to-machine-learning/
Which machine learning algorithm should I use? (sas.com)
https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
The Machine Learning Primer (sas.com)
https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/machine-learning-primer-108796.pdf
Machine Learning Tutorial for Beginners (kaggle.com/kanncaa1)
https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners
What is the role of the activation 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 of activation 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 Logarithmic Loss (exegetic.biz)
http://www.exegetic.biz/blog/2015/12/making-sense-logarithmic-loss/
Loss Functions (Stanford CS231n)
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 cost function (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function
1.2 偏差(bias)
Role of Bias in Neural Networks (stackoverflow.com)
https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks/2499936#2499936
Bias Nodes in Neural Networks (makeyourownneuralnetwork.blogspot.com)
http://makeyourownneuralnetwork.blogspot.com/2016/06/bias-nodes-in-neural-networks.html
What is bias in artificial neural network? (quora.com)
https://www.quora.com/What-is-bias-in-artificial-neural-network
From Perceptrons to Deep Networks (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 linear regression analysis (duke.edu)
http://people.duke.edu/~rnau/regintro.htm
Linear Regression (ufldl.stanford.edu)
http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/
Linear Regression (readthedocs.io)
http://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html
Simple Linear RegressionTutorial for Machine Learning (machinelearningmastery.com)
http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/
Logistic Regression Tutorial for Machine Learning(machinelearningmastery.com)
http://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/
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 back propagation algorithm 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/
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/
Deep Learning and Convolutional Neural 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/
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/
An Overview of Multi-TaskLearning in Deep Neural Networks (sebastianruder.com)
http://sebastianruder.com/multi-task/index.html
第二部分:自然语言处理
Natural Language Processing isFun! (medium.com/@ageitgey)
https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e
A Primer on Neural Network Models for Natural Language Processing (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/
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/
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
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
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/
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/