超級大匯總！200多個最好的機器學(xué)習(xí)、NLP和Python教程
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超級大匯總！200多個最好的機器學(xué)習(xí)、NLP和Python教程。本文分為四個部分：機器學(xué)習(xí)，自然語言處理，python和數(shù)學(xué)。
超級大匯總！200多個最好的機器學(xué)習(xí)、NLP和
Python教程。本文分為四個部分：機器學(xué)習(xí)，自然語言處理，python和數(shù)學(xué)。
機器學(xué)習(xí)
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 Crash Course: Part I, Part II, Part III (Machine Learning at Berkeley)
https://ml.berkeley.edu/blog/2016/11/06/tutorial-1/
https://ml.berkeley.edu/blog/2016/12/24/tutorial-2/
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/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
激活和損失函數(shù)
Sigmoid neurons (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#sigmoid_neurons
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’s types-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 Loss function (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
偏差
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
感知機
Perceptrons (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#perceptrons
The Perception (natureofcode.com)
https://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 Deep Networks (toptal.com)
https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks
回歸
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
Logistic Regression (readthedocs.io)
https://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html
Simple Linear Regression Tutorial for Machine Learning(machinelearningmastery.com)
http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/
Logistic Regression Tutorial for Machine Learning(machinelearningmastery.com)
https://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/
Softmax Regression (ufldl.stanford.edu)
http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression/
梯度下降
Learning with gradient descent (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#learning_with_gradient_descent
Gradient Descent (iamtrask.github.io)
http://iamtrask.github.io/2015/07/27/python-network-part2/
How to understand Gradient Descent algorithm (kdnuggets.com)
http://www.kdnuggets.com/2017/04/simple-understand-gradient-descent-algorithm.html
An overview of gradient descent optimization algorithms(sebastianruder.com)
http://sebastianruder.com/optimizing-gradient-descent/
Optimization: Stochastic Gradient Descent (Stanford CS231n)
http://cs231n.github.io/optimization-1/
生成學(xué)習(xí)
Generative Learning Algorithms (Stanford CS229)
http://cs229.stanford.edu/notes/cs229-notes2.pdf
A practical explanation of a Naive Bayes classifier (monkeylearn.com)
https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/
支持向量機
An introduction to Support Vector Machines (SVM) (monkeylearn.com)
https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/
Support Vector Machines (Stanford CS229)
http://cs229.stanford.edu/notes/cs229-notes3.pdf
Linear classification: Support Vector Machine, Softmax (Stanford 231n)
http://cs231n.github.io/linear-classify/
深度學(xué)習(xí)
A Guide to Deep Learning by YN? (yerevann.com)
http://yerevann.com/a-guide-to-deep-learning/
Deep Learning Papers Reading Roadmap (github.com/floodsung)
https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap
Deep Learning in a Nutshell (nikhilbuduma.com)
http://nikhilbuduma.com/2014/12/29/deep-learning-in-a-nutshell/
A Tutorial on Deep Learning (Quoc V. Le)
http://ai.stanford.edu/~quocle/tutorial1.pdf
What is Deep Learning? (machinelearningmastery.com)
https://machinelearningmastery.com/what-is-deep-learning/
What’s the Difference Between Artificial 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?—?The Straight Dope (gluon.mxnet.io)
https://gluon.mxnet.io/
優(yōu)化和降維
Seven Techniques for Data Dimensionality Reduction (knime.org)
https://www.knime.org/blog/seven-techniques-for-data-dimensionality-reduction
Principal components analysis (Stanford CS229)
http://cs229.stanford.edu/notes/cs229-notes10.pdf
Dropout: A simple way to improve neural networks (Hinton @ NIPS 2012)
http://cs229.stanford.edu/notes/cs229-notes10.pdf
How to train your Deep Neural Network (rishy.github.io)
http://rishy.github.io/ml/2017/01/05/how-to-train-your-dnn/
長短期記憶（LSTM）
A Gentle Introduction to Long Short-Term Memory Networks by the Experts(machinelearningmastery.com)
https://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/
Understanding LSTM Networks (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 an LSTM-RNN in Python (iamtrask.github.io)
http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/
卷積神經(jīng)網(wǎng)絡(luò)
Introducing convolutional networks (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap6.html#introducing_convolutional_networks
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 Modular Perspective (colah.github.io)
http://colah.github.io/posts/2014-07-Conv-Nets-Modular/
Understanding Convolutions (colah.github.io)
http://colah.github.io/posts/2014-07-Understanding-Convolutions/
遞歸神經(jīng)網(wǎng)絡(luò)
Recurrent Neural Networks Tutorial (wildml.com)
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
Attention and Augmented Recurrent Neural Networks (distill.pub)
http://distill.pub/2016/augmented-rnns/
The Unreasonable Effectiveness of Recurrent Neural Networks(karpathy.github.io)
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
A Deep Dive into Recurrent Neural Nets (nikhilbuduma.com)
http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/
強化學(xué)習(xí)
Simple Beginner’s guide to Reinforcement Learning & its implementation(analyticsvidhya.com)
https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/
A Tutorial for Reinforcement Learning (mst.edu)
https://web.mst.edu/~gosavia/tutorial.pdf
Learning Reinforcement Learning (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/
生成對抗網(wǎng)絡(luò)（GANs）
Adversarial Machine Learning (aaai18adversarial.github.io)
https://aaai18adversarial.github.io/slides/AML.pptx
What’s a Generative Adversarial Network? (nvidia.com)
https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network/
Abusing Generative Adversarial Networks 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 Generative Adversarial Networks (with code in TensorFlow) (aylien.com)
http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/
Generative Adversarial Networks for Beginners (oreilly.com)
https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners
多任務(wù)學(xué)習(xí)
An Overview of Multi-Task Learning in Deep Neural Networks(sebastianruder.com)
http://sebastianruder.com/multi-task/index.html
自然語言處理
Natural Language Processing is Fun! (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 Natural Language Processing (monkeylearn.com)
https://monkeylearn.com/blog/the-definitive-guide-to-natural-language-processing/
Introduction to Natural Language 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
深度學(xué)習(xí)和自然語言處理
Deep Learning applied to NLP (arxiv.org)
https://arxiv.org/pdf/1703.03091.pdf
Deep Learning for NLP (without Magic) (Richard Socher)
https://nlp.stanford.edu/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdf
Understanding Convolutional Neural Networks for NLP (wildml.com)
http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/
Deep Learning, NLP, and Representations (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 NLP models (explosion.ai)
https://explosion.ai/blog/deep-learning-formula-nlp
Understanding Natural Language with Deep Neural Networks Using Torch(nvidia.com)
https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/
Deep Learning for NLP with Pytorch (pytorich.org)
http://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html
詞向量
Bag of Words Meets Bags of Popcorn (kaggle.com)
https://www.kaggle.com/c/word2vec-nlp-tutorial
On word embeddings Part I, Part II, Part III (sebastianruder.com)
http://sebastianruder.com/word-embeddings-1/index.html
http://sebastianruder.com/word-embeddings-softmax/index.html
http://sebastianruder.com/secret-word2vec/index.html
The amazing power of word vectors (acolyer.org)
https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/
word2vec Parameter Learning Explained (arxiv.org)
https://arxiv.org/pdf/1411.2738.pdf
Word2Vec Tutorial?—?The Skip-Gram Model, Negative Sampling(mccormickml.com)
http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/
編碼器-解碼器
Attention and Memory in Deep Learning and NLP (wildml.com)
http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/
Sequence to Sequence Models (tensorflow.org)
https://www.tensorflow.org/tutorials/seq2seq
Sequence to Sequence Learning with 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 of Sequences (medium.com/@ageitgey)
https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa
tf-seq2seq (google.github.io)
https://google.github.io/seq2seq/
Python
Machine Learning Crash Course (google.com)
https://developers.google.com/machine-learning/crash-course/
Awesome Machine Learning (github.com/josephmisiti)
https://github.com/josephmisiti/awesome-machine-learning#python
7 Steps to Mastering Machine Learning With Python (kdnuggets.com)
http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html
An example machine learning notebook (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 with Python (tutorialspoint.com)
https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_quick_guide.htm
范例
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 of Python (iamtrask.github.io)
http://iamtrask.github.io/2015/07/12/basic-python-network/
Implementing Your Own k-Nearest Neighbour Algorithm Using Python(kdnuggets.com)
http://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html
ML from Scatch (github.com/eriklindernoren)
https://github.com/eriklindernoren/ML-From-Scratch
Python Machine Learning (2nd Ed.) Code Repository (github.com/rasbt)
https://github.com/rasbt/python-machine-learning-book-2nd-edition
Scipy and numpy
Scipy Lecture Notes (scipy-lectures.org)
http://www.scipy-lectures.org/
Python Numpy Tutorial (Stanford CS231n)
http://cs231n.github.io/python-numpy-tutorial/
An introduction to Numpy and Scipy (UCSB CHE210D)
https://engineering.ucsb.edu/~shell/che210d/numpy.pdf
A Crash Course in Python for Scientists (nbviewer.jupyter.org)
http://nbviewer.jupyter.org/gist/rpmuller/5920182#ii.-numpy-and-scipy
scikit-learn
PyCon scikit-learn Tutorial Index (nbviewer.jupyter.org)
http://nbviewer.jupyter.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb
scikit-learn Classification Algorithms (github.com/mmmayo13)
https://github.com/mmmayo13/scikit-learn-classifiers/blob/master/sklearn-classifiers-tutorial.ipynb
scikit-learn Tutorials (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
Tensorflow
Tensorflow Tutorials (tensorflow.org)
https://www.tensorflow.org/tutorials/
Introduction to TensorFlow?—?CPU vs GPU (medium.com/@erikhallstrm)
https://medium.com/@erikhallstrm/hello-world-tensorflow-649b15aed18c
TensorFlow: A primer (metaflow.fr)
https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3
RNNs in Tensorflow (wildml.com)
http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/
Implementing a CNN for Text Classification in TensorFlow (wildml.com)
http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
How to Run Text Summarization with TensorFlow (surmenok.com)
http://pavel.surmenok.com/2016/10/15/how-to-run-text-summarization-with-tensorflow/
PyTorch
PyTorch Tutorials (pytorch.org)
http://pytorch.org/tutorials/
A Gentle Intro to PyTorch (gaurav.im)
http://blog.gaurav.im/2017/04/24/a-gentle-intro-to-pytorch/
Tutorial: Deep Learning in PyTorch (iamtrask.github.io)
https://iamtrask.github.io/2017/01/15/pytorch-tutorial/
PyTorch Examples (github.com/jcjohnson)
https://github.com/jcjohnson/pytorch-examples
PyTorch Tutorial (github.com/MorvanZhou)
https://github.com/MorvanZhou/PyTorch-Tutorial
PyTorch Tutorial for Deep Learning Researchers (github.com/yunjey)
https://github.com/yunjey/pytorch-tutorial
數(shù)學(xué)
Math for Machine Learning (ucsc.edu)
https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf
Math for Machine Learning (UMIACS CMSC422)
http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf
線性代數(shù)
An Intuitive Guide to Linear Algebra (betterexplained.com)
https://betterexplained.com/articles/linear-algebra-guide/
A Programmer’s Intuition for Matrix Multiplication (betterexplained.com)
https://betterexplained.com/articles/matrix-multiplication/
Understanding the Cross Product (betterexplained.com)
https://betterexplained.com/articles/cross-product/
Understanding the Dot Product (betterexplained.com)
https://betterexplained.com/articles/vector-calculus-understanding-the-dot-product/
Linear Algebra for Machine Learning (U. of Buffalo CSE574)
http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/LinearAlgebra.pdf
Linear algebra cheat sheet for deep learning (medium.com)
https://medium.com/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c
Linear Algebra Review and Reference (Stanford CS229)
http://cs229.stanford.edu/section/cs229-linalg.pdf
概率
Understanding Bayes Theorem With Ratios (betterexplained.com)
https://betterexplained.com/articles/understanding-bayes-theorem-with-ratios/
Review of Probability Theory (Stanford CS229)
http://cs229.stanford.edu/section/cs229-prob.pdf
Probability Theory Review for Machine Learning (Stanford CS229)
https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf
Probability Theory (U. of Buffalo CSE574)
http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/Probability-Theory.pdf
Probability Theory for Machine Learning (U. of Toronto CSC411)
http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial1.pdf
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: Understanding the Gradient (betterexplained.com)
https://betterexplained.com/articles/vector-calculus-understanding-the-gradient/
Differential Calculus (Stanford CS224n)
http://web.stanford.edu/class/cs224n/lecture_notes/cs224n-2017-review-differential-calculus.pdf
Calculus Overview (readthedocs.io)
http://ml-cheatsheet.readthedocs.io/en/latest/calculus.html
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