本帖最后由 Killoser 于 2018-2-2 10:27 编辑

机器学习

1、机器学习就是这么好玩！(medium.com/@ageitgey)


机器学习速成课程（Berkeley的ML）：

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/


机器学习入门与应用：实例图解(toptal.com)

https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer



机器学习的简易指南 (monkeylearn.com)

https://monkeylearn.com/blog/a-gentle-guide-to-machine-learning/



如何选择机器学习算法？(sas.com)

https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/



2、Activation and Loss Functions

激活函数与损失函数


sigmoid 神经元 (neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap1.html#sigmoid_neurons



激活函数在神经网络中有什么作用？(quora.com)

https://www.quora.com/What-is-the-role-of-the-activation-function-in-a-neural-network



神经网络的激活函数大全及其优劣 (stats.stackexchange.com)

https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-cons



激活函数及其分类比较(medium.com)

https://medium.com/towards-data-science/activation-functions-and-its-types-which-is-better-a9a5310cc8f



理解对数损失 (exegetic.biz)

http://www.exegetic.biz/blog/2015/12/making-sense-logarithmic-loss/



损失函数(Stanford CS231n)

http://cs231n.github.io/neural-networks-2/#losses



损失函数L1 与L2 比较(rishy.github.io)

http://rishy.github.io/ml/2015/07/28/l1-vs-l2-loss/



交叉熵损失函数(neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function



3、偏差（Bias）


神经网络中的偏差的作用(stackoverflow.com)

https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks/2499936#2499936



神经网络中的偏差节点(makeyourownneuralnetwork.blogspot.com)

http://makeyourownneuralnetwork.blogspot.com/2016/06/bias-nodes-in-neural-networks.html



什么是人工神经网络中的偏差 (quora.com)

https://www.quora.com/What-is-bias-in-artificial-neural-network



4、感知器（Perceptron）


感知器模型(neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap1.html#perceptrons



感知器(natureofcode.com)

http://natureofcode.com/book/chapter-10-neural-networks/#chapter10_figure3



一层的神经网络（感知器模型）(dcu.ie)

http://computing.dcu.ie/~humphrys/Notes/Neural/single.neural.html



从感知器模型到深度网络(toptal.com)

https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks



5、回归算法


线性回归分析简介(duke.edu)

http://people.duke.edu/~rnau/regintro.htm



线性回归 (ufldl.stanford.edu)

http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/



线性回归 (readthedocs.io)

http://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html



逻辑斯特回归 (readthedocs.io)

http://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html



机器学习之简单线性回归教程(machinelearningmastery.com)

http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/



机器学习之逻辑斯特回归教程(machinelearningmastery.com)

http://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/



softmax 回归(ufldl.stanford.edu)

http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression/




6、梯度下降


基于梯度下降的学习 (neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap1.html#learning_with_gradient_descent



梯度下降(iamtrask.github.io)

http://iamtrask.github.io/2015/07/27/python-network-part2/



如何理解梯度下降算法？(kdnuggets.com)

http://www.kdnuggets.com/2017/04/simple-understand-gradient-descent-algorithm.html



梯度下降优化算法概览(sebastianruder.com)

http://sebastianruder.com/optimizing-gradient-descent/



优化算法：随机梯度下降算法 (Stanford CS231n)

http://cs231n.github.io/optimization-1/



7、生成学习



生成学习算法 (Stanford CS229)

http://cs229.stanford.edu/notes/cs229-notes2.pdf



贝叶斯分类算法之实例解析(monkeylearn.com)

https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/



8、支持向量机



支持向量机（SVM）入门(monkeylearn.com)

https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/



支持向量机(Stanford CS229)

http://cs229.stanford.edu/notes/cs229-notes3.pdf



线性分类：支持向量机，Softmax (Stanford 231n)

http://cs231n.github.io/linear-classify/



9、后向传播算法（Backpropagation）



后向传播算法必知(medium.com/@karpathy)

https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b



来，给我图解一下神经网络后向传播算法？(github.com/rasbt)

https://github.com/rasbt/python-machine-learning-book/blob/master/faq/visual-backpropagation.md



后向传播算法是如何运行的？(neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap2.html



沿时后向传播算法与梯度消失(wildml.com)

http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/



简易入门沿时后向传播算法(machinelearningmastery.com)

http://machinelearningmastery.com/gentle-introduction-backpropagation-time/



奔跑吧，后向传播算法！(Stanford CS231n)

http://cs231n.github.io/optimization-2/



10、深度学习



果壳里的深度学习(nikhilbuduma.com)

http://nikhilbuduma.com/2014/12/29/deep-learning-in-a-nutshell/



深度学习教程 (Quoc V. Le)

http://ai.stanford.edu/~quocle/tutorial1.pdf



深度学习，什么鬼？(machinelearningmastery.com)

http://machinelearningmastery.com/what-is-deep-learning/



什么是人工智能，机器学习，深度学习之间的区别？ (nvidia.com)

https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/



11、优化算法与降维算法



数据降维的七招炼金术(knime.org)

https://www.knime.org/blog/seven-techniques-for-data-dimensionality-reduction



主成分分析(Stanford CS229)

http://cs229.stanford.edu/notes/cs229-notes10.pdf



Dropout: 改进神经网络的一个简单方法(Hinton @ NIPS 2012)

http://videolectures.net/site/normal_dl/tag=741100/nips2012_hinton_networks_01.pdf



如何溜你们家的深度神经网络？(rishy.github.io)

http://rishy.github.io/ml/2017/01/05/how-to-train-your-dnn/




12、长短期记忆(LSTM)


老司机带你简易入门长短期神经网络(machinelearningmastery.com)

http://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/



理解LSTM网络(colah.github.io)

http://colah.github.io/posts/2015-08-Understanding-LSTMs/



漫谈LSTM模型(echen.me)

http://blog.echen.me/2017/05/30/exploring-lstms/



小学生看完这教程都可以用Python实现一个LSTM-RNN (iamtrask.github.io)

http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/



13、卷积神经网络（CNNs）



卷积网络入门(neuralnetworksanddeeplearning.com)

http://neuralnetworksanddeeplearning.com/chap6.html#introducing_convolutional_networks



深度学习与卷积神经网络模型(medium.com/@ageitgey)

https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721



拆解卷积网络模型(colah.github.io)

http://colah.github.io/posts/2014-07-Conv-Nets-Modular/



理解卷积网络(colah.github.io)

http://colah.github.io/posts/2014-07-Understanding-Convolutions/


14、递归神经网络(RNNs)



递归神经网络教程 (wildml.com)

http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/



注意力模型与增强型递归神经网络(distill.pub)

http://distill.pub/2016/augmented-rnns/



这么不科学的递归神经网络模型(karpathy.github.io)

http://karpathy.github.io/2015/05/21/rnn-effectiveness/



深入递归神经网络模型(nikhilbuduma.com)

http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/


15、强化学习



给小白看的强化学习及其实现指南 (analyticsvidhya.com)

https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/



强化学习教程(mst.edu)

https://web.mst.edu/~gosavia/tutorial.pdf



强化学习，你学了么？(wildml.com)

http://www.wildml.com/2016/10/learning-reinforcement-learning/



深度强化学习：开挂玩Pong (karpathy.github.io)

http://karpathy.github.io/2016/05/31/rl/



16、对抗式生成网络模型(GANs)



什么是对抗式生成网络模型？(nvidia.com)

https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network/



用对抗式生成网络创造8个像素的艺术(medium.com/@ageitgey)

https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7



对抗式生成网络入门（TensorFlow）(aylien.com)

http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/



《对抗式生成网络》（小学一年级~上册）(oreilly.com)

https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners



17、多任务学习


深度神经网络中的多任务学习概述(sebastianruder.com)

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



  NLP


1、NLP


《基于神经网络模型的自然语言处理》（小学一年级~上册）(Yoav Goldberg)

http://u.cs.biu.ac.il/~yogo/nnlp.pdf



自然语言处理权威指南(monkeylearn.com)

https://monkeylearn.com/blog/the-definitive-guide-to-natural-language-processing/



自然语言处理入门(algorithmia.com)

https://blog.algorithmia.com/introduction-natural-language-processing-nlp/



自然语言处理教程 (vikparuchuri.com)

http://www.vikparuchuri.com/blog/natural-language-processing-tutorial/



Natural Language Processing (almost) from Scratch (arxiv.org)

初高中生课程：自然语言处理 (arxiv.org)

https://arxiv.org/pdf/1103.0398.pdf  


2、深度学习和 NLP


基于深度学习的NLP应用(arxiv.org)

https://arxiv.org/pdf/1703.03091.pdf



基于深度学习的NLP(Richard Socher)

https://nlp.stanford.edu/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdf



理解卷积神经网络在NLP中的应用(wildml.com)

http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/



深度学习，NLP，表示学习(colah.github.io)

http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/



嵌入表示，编码，注意力，预测 : 新一代深度学习因NLP的精妙而存在(explosion.ai)

https://explosion.ai/blog/deep-learning-formula-nlp



理解基于神经网络的自然语言处理(Torch实现) (nvidia.com)

https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/



深度学习在NLP中的应用(Pytorch实现) (pytorich.org)

http://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html



3、词向量（Word Vectors）



词袋法遇到感知器装袋法(kaggle.com)

https://www.kaggle.com/c/word2vec-nlp-tutorial



学习单词嵌入表示法(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



单词嵌入表示的神奇力量(acolyer.org)

https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/



解释word2vec 的参数学习(arxiv.org)

https://arxiv.org/pdf/1411.2738.pdf



word2vec教程 skip-gram 模型，负采样(mccormickml.com)

http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/



4、Encoder-Decoder



注意力机制与记忆机制在深度学习与NLP中的应用(wildml.com)

http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/



序列到序列模型(tensorflow.org)

https://www.tensorflow.org/tutorials/seq2seq



利用神经网络学习序列到序列模型(NIPS 2014)

https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf



基于深度学习和魔法序列的语言翻译(medium.com/@ageitgey)

https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa



如何使用编码-解码LSTM输出随机整数对应的序列(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/



  Python

1、Python


使用Python精通机器学习的七步法(kdnuggets.com)

http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html



机器学习的一个简例(nbviewer.jupyter.org)

http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example Machine Learning Notebook.ipynb


2、实例


小白如何用python实现感知器算法(machinelearningmastery.com)

http://machinelearningmastery.com/implement-perceptron-algorithm-scratch-python/



小学生用python实现一个神经网络(wildml.com)

http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/



只用11行python代码实现一个神经网络算法(iamtrask.github.io)

http://iamtrask.github.io/2015/07/12/basic-python-network/



自己动手用ptython实现最近邻算法(kdnuggets.com)

http://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html



python实现长短期记忆网络的记忆机制(machinelearningmastery.com)

http://machinelearningmastery.com/memory-in-a-long-short-term-memory-network/



如何用长短期记忆递归神经网络输出随机整数(machinelearningmastery.com)

http://machinelearningmastery.com/learn-echo-random-integers-long-short-term-memory-recurrent-neural-networks/



如何用seq2seq递归神经网络学习加法运算(machinelearningmastery.com)

http://machinelearningmastery.com/learn-add-numbers-seq2seq-recurrent-neural-networks/



3、Scipy 和 numpy



Scipy课程笔记(scipy-lectures.org)

http://www.scipy-lectures.org/



Python Numpy 教程(Stanford CS231n)

http://cs231n.github.io/python-numpy-tutorial/



Numpy 与 Scipy 入门(UCSB CHE210D)

https://engineering.ucsb.edu/~shell/che210d/numpy.pdf



给科学家看的Python微课程(nbviewer.jupyter.org)

http://nbviewer.jupyter.org/gist/rpmuller/5920182#ii.-numpy-and-scipy



4、scikit-learn



PyCon会议上的Scik-learn 教程(nbviewer.jupyter.org)

http://nbviewer.jupyter.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb



Scikit-learn 中的分类算法(github.com/mmmayo13)

https://github.com/mmmayo13/scikit-learn-classifiers/blob/master/sklearn-classifiers-tutorial.ipynb



Scikit-learn教程(scikit-learn.org)

http://scikit-learn.org/stable/tutorial/index.html



简明版Scikit-learn教程(github.com/mmmayo13)

https://github.com/mmmayo13/scikit-learn-beginners-tutorials



5、Tensorflow



Tensorflow教程(tensorflow.org)

https://www.tensorflow.org/tutorials/



Tensorflow入门--CPU vs GPU

(medium.com/@erikhallstrm)

https://medium.com/@erikhallstrm/hello-world-tensorflow-649b15aed18c



Tensorflow入门(metaflow.fr)

https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3



Tensorflow实现RNNs (wildml.com)

http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/



Tensorflow实现文本分类CNN模型(wildml.com)

http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/



如何用Tensorflow做文本摘要(surmenok.com)

http://pavel.surmenok.com/2016/10/15/how-to-run-text-summarization-with-tensorflow/



6、PyTorch



Pytorch教程(pytorch.org)

http://pytorch.org/tutorials/


Pytorch快手入门 (gaurav.im)

http://blog.gaurav.im/2017/04/24/a-gentle-intro-to-pytorch/



利用Pytorch深度学习教程(iamtrask.github.io)

https://iamtrask.github.io/2017/01/15/pytorch-tutorial/



Pytorch实战(github.com/jcjohnson)

https://github.com/jcjohnson/pytorch-examples



PyTorch 教程(github.com/MorvanZhou)

https://github.com/MorvanZhou/PyTorch-Tutorial



深度学习研究人员看的PyTorch教程(github.com/yunjey)

https://github.com/yunjey/pytorch-tutorial




  数学


1、机器学习中的数学 (ucsc.edu)

https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf



机器学习数学基础(UMIACS CMSC422)

http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf



2、线性代数



线性代数简明指南(betterexplained.com)

https://betterexplained.com/articles/linear-algebra-guide/



码农眼中矩阵乘法 (betterexplained.com)

https://betterexplained.com/articles/matrix-multiplication/



理解叉乘运算(betterexplained.com)

https://betterexplained.com/articles/cross-product/



理解点乘运算(betterexplained.com)

https://betterexplained.com/articles/vector-calculus-understanding-the-dot-product/



机器学习中的线性代数(U. of Buffalo CSE574)

http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/LinearAlgebra.pdf



深度学习的线代小抄(medium.com)

https://medium.com/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c



复习线性代数与课后阅读材料(Stanford CS229)

http://cs229.stanford.edu/section/cs229-linalg.pdf



3、概率论



贝叶斯理论 (betterexplained.com)

https://betterexplained.com/articles/understanding-bayes-theorem-with-ratios/



理解贝叶斯概率理论(Stanford CS229)

http://cs229.stanford.edu/section/cs229-prob.pdf



复习机器学习中的概率论(Stanford CS229)

https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf



概率论(U. of Buffalo CSE574)

http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/Probability-Theory.pdf



机器学习中的概率论(U. of Toronto CSC411)

http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial1.pdf


4、计算方法（Calculus）



如何理解导数：求导法则，指数和算法(betterexplained.com)

https://betterexplained.com/articles/how-to-understand-derivatives-the-quotient-rule-exponents-and-logarithms/



如何理解导数，乘法，幂指数，链式法(betterexplained.com)

https://betterexplained.com/articles/derivatives-product-power-chain/



向量计算，理解梯度(betterexplained.com)

https://betterexplained.com/articles/vector-calculus-understanding-the-gradient/



微分计算(Stanford CS224n)

http://web.stanford.edu/class/cs224n/lecture_notes/cs224n-2017-review-differential-calculus.pdf



计算方法概论(readthedocs.io)

http://ml-cheatsheet.readthedocs.io/en/latest/calculus.html

本文英文出处：Robbie Allen

翻译/雷锋网