本文分享一些机器学习和深度学习等等学习资料，便于翻阅及进阶自身技术

机器学习

• 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

激活和损失函数

• 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

感知机

• 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

回归

• 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/

生成学习

• 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/

支持向量机

• 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/

深度学习

• A Guide to Deep Learning by YN² (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/

优化和降维

• 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/

长短期记忆(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/

卷积神经网络

• 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/

递归神经网络

• 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/

强化学习

• 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/

生成对抗网络(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

多任务学习

• An Overview of Multi-Task Learning in Deep Neural Networks(sebastianruder.com)

  http://sebastianruder.com/multi-task/index.html

自然语言处理

• 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

深度学习和自然语言处理

• 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

词向量

• 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/

编码器-解码器

• 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

数学

• 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

线性代数

• 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: 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