摘要:不吹不黑,绝对史上最全的机器学习学习材料!本文包含了迄今为止大家公认的最佳教程内容。它绝不是网上每个ML相关教程的详尽列表,而是经过精挑细选而成的,毕竟网上的东西并不全是好的。作者汇总的目标是为了补充我即将出版的新书,为它寻找在机器学习和NLP领域中找到的最佳教程。
通过这些最佳教程的汇总,我可以快速的找到我想要得到的教程。从而避免了阅读更广泛覆盖范围的书籍章节和苦恼的研究论文,你也许知道,当你的数学功底不是很好的时候这些论文你通常是拿不下的。为什么不买书呢?没有哪一个作者是一个全能先生。当你尝试学习特定的主题或想要获得不同的观点时,教程可能是非常有帮助的。
我将这篇文章分为四个部分:机器学习,NLP,Python和数学。我在每个部分都包含了一些主题,但由于机器学习是一个非常复杂的学科,我不可能包含所有可能的主题。
如果有很好的教程你知道我错过了,请告诉我!我将继续完善这个学习教程。我在挑选这些链接的时候,都试图保证每个链接应该具有与其他链接不同的材料或以不同的方式呈现信息(例如,代码与幻灯片)或从不同的角度。
机器学习
从机器学习入手
https://machinelearningmastery.com/start-here/
机器学习很有趣!
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
机器学习规则:ML工程的最佳实践
http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
机器学习速成课程:第一部分,第二部分,第三部分(伯克利机器学习)
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/
机器学习理论及其应用简介:用一个小例子进行视觉教程
https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer
机器学习的简单指南
https://monkeylearn.com/blog/a-gentle-guide-to-machine-learning/
我应该使用哪种机器学习算法?
https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
机器学习入门
https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/machine-learning-primer-108796.pdf
初学者机器学习教程
https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners
激活函数和Dropout函数
Sigmoid神经元
http://neuralnetworksanddeeplearning.com/chap1.html
激活函数在神经网络中的作用是什么?
https://www.quora.com/What-is-the-role-of-the-activation-function-in-a-neural-network
神经网络中常见的激活函数的优缺点比较列表
https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-cons
激活函数及其类型对比
https://medium.com/towards-data-science/activation-functions-and-its-types-which-is-better-a9a5310cc8f
理解对数损失
http://www.exegetic.biz/blog/2015/12/making-sense-logarithmic-loss/
损失函数(斯坦福CS231n)
http://cs231n.github.io/neural-networks-2/
L1与L2损失函数
http://rishy.github.io/ml/2015/07/28/l1-vs-l2-loss/
交叉熵成本函数
http://neuralnetworksanddeeplearning.com/chap3.html
偏差(bias)
偏差在神经网络中的作用
https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks/2499936
神经网络中的偏差节点
http://makeyourownneuralnetwork.blogspot.com/2016/06/bias-nodes-in-neural-networks.html
什么是人工神经网络的偏差?
https://www.quora.com/What-is-bias-in-artificial-neural-network
感知器
感知器
http://neuralnetworksanddeeplearning.com/chap1.html
感知器
http://natureofcode.com/book/chapter-10-neural-networks/
单层神经网络(感知器)
http://computing.dcu.ie/~humphrys/Notes/Neural/single.neural.html
从Perceptrons到Deep Networks
https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks
回归
线性回归分析介绍
http://people.duke.edu/~rnau/regintro.htm
线性回归
http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/
线性回归
http://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html
Logistic回归
http://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html
机器学习的简单线性回归教程
http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/
机器学习的Logistic回归教程
http://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/
Softmax回归
http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression/
梯度下降
在梯度下降中学习
http://neuralnetworksanddeeplearning.com/chap1.html
梯度下降
http://iamtrask.github.io/2015/07/27/python-network-part2/
如何理解梯度下降算法
http://www.kdnuggets.com/2017/04/simple-understand-gradient-descent-algorithm.html
梯度下降优化算法概述
http://sebastianruder.com/optimizing-gradient-descent/
优化:随机梯度下降(斯坦福CS231n)
http://cs231n.github.io/optimization-1/
生成学习(GenerativeLearning)
生成学习算法(斯坦福CS229)
http://cs229.stanford.edu/notes/cs229-notes2.pdf
朴素贝叶斯分类器的实用解释
https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/
https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/
支持向量机
支持向量机(SVM)简介
https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/
支持向量机(斯坦福CS229)
http://cs229.stanford.edu/notes/cs229-notes3.pdf
线性分类:支持向量机,Softmax
http://cs231n.github.io/linear-classify/
反向传播
你应该了解的backprop
(medium.com/@karpathy)
你能给出神经网络反向传播算法的直观解释吗?
https://github.com/rasbt/python-machine-learning-book/blob/master/faq/visual-backpropagation.md
反向传播算法的工作原理
http://neuralnetworksanddeeplearning.com/chap2.html
通过时间反向传播和消失的渐变
http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/
http://www.wildml.com/2015/10/recurrent-neural-networks-tutorial-part-3-backpropagation-through-time-and-vanishing-gradients/
时间反向传播的简单介绍
http://machinelearningmastery.com/gentle-introduction-backpropagation-time/
反向传播,直觉(斯坦福CS231n)
http://cs231n.github.io/optimization-2/
深度学习
YN²深度学习指南
http://cs231n.github.io/optimization-2/
深度学习论文阅读路线图
https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap
Nutshell中的深度学习
http://nikhilbuduma.com/2014/12/29/deep-learning-in-a-nutshell/
深度学习教程
http://ai.stanford.edu/~quocle/tutorial1.pdf
什么是深度学习?
http://machinelearningmastery.com/what-is-deep-learning/
人工智能,机器学习和深度学习之间有什么区别?
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
深度学习–简单介绍
https://gluon.mxnet.io/
最优化和降维
数据降维减少的七种技术
https://www.knime.org/blog/seven-techniques-for-data-dimensionality-reduction
主成分分析(斯坦福CS229)
http://cs229.stanford.edu/notes/cs229-notes10.pdf
Dropout:一种改善神经网络的简单方法http://videolectures.net/site/normal_dl/tag=741100/nips2012_hinton_networks_01.pdf
如何训练你的深度神经网络?
http://rishy.github.io/ml/2017/01/05/how-to-train-your-dnn/
长短期记忆(LSTM)
长短期记忆网络的通俗介绍
http://machinelearningmastery.com/gentle-introduction-long-short-term-memory-networks-experts/
了解LSTM 神经网络Networks
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
探索LSTM
http://blog.echen.me/2017/05/30/exploring-lstms/
任何人都可以学习用Python编写LSTM-RNN
http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/
http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/
卷积神经网络(CNN)
卷积网络介绍
http://neuralnetworksanddeeplearning.com/chap6.html
深度学习和卷积神经网络
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721
Conv Nets:模块化视角
http://colah.github.io/posts/2014-07-Conv-Nets-Modular/
了解卷积
http://colah.github.io/posts/2014-07-Understanding-Convolutions/
递归神经网络(RNN)
递归神经网络教程
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
注意和增强的递归神经网络
http://distill.pub/2016/augmented-rnns/
递归神经网络的不合理有效性
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
深入了解递归神经网络
http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/
http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/
强化学习
强化学习初学者入门及其实施指南
https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/
强化学习教程
https://web.mst.edu/~gosavia/tutorial.pdf
学习强化学习
http://www.wildml.com/2016/10/learning-reinforcement-learning/
深度强化学习:来自像素的乒乓球
http://karpathy.github.io/2016/05/31/rl/
生成对抗网络(GAN)
对抗机器学习简介
https://aaai18adversarial.github.io/slides/AML.pptx
什么是生成性对抗网络?
https://blogs.nvidia.com/blog/2017/05/17/generative-adversarial-network/
滥用生成对抗网络制作8位像素艺术
https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7
Generative Adversarial Networks简介(TensorFlow中的代码)
http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/
初学者的生成对抗网络
https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners
多任务学习
深度神经网络中多任务学习概述
http://sebastianruder.com/multi-task/index.html
NLP
自然语言处理很有趣!
https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e
自然语言处理神经网络模型入门
http://u.cs.biu.ac.il/~yogo/nnlp.pdf
自然语言处理权威指南
https://monkeylearn.com/blog/the-definitive-guide-to-natural-language-processing/
自然语言处理简介
https://blog.algorithmia.com/introduction-natural-language-processing-nlp/
自然语言处理教程
http://www.vikparuchuri.com/blog/natural-language-processing-tutorial/
自然语言处理(NLP)来自Scratch
https://arxiv.org/pdf/1103.0398.pdf
深度学习和NLP
深度学习适用于NLP
https://arxiv.org/pdf/1703.03091.pdf
NLP的深度学习(没有魔法)
https://nlp.stanford.edu/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdf
了解NLP的卷积神经网络
http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/
深度学习、NLP、表示
http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/
最先进的NLP模型的新深度学习公式:嵌入、编码、参与、预测
https://explosion.ai/blog/deep-learning-formula-nlp
使用Torch深度神经网络进行自然语言处理
https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/
使用Pytorch进行深度学习NLP
http://pytorch.org/tutorials/beginner/deep_learning_nlp_tutorial.html
词向量
使用词袋模型解决电影评论分类
https://www.kaggle.com/c/word2vec-nlp-tutorial
词嵌入介绍第一部分,第二部分,第三部分
http://sebastianruder.com/word-embeddings-1/index.html
http://sebastianruder.com/word-embeddings-softmax/index.html
http://sebastianruder.com/secret-word2vec/index.html
词向量的惊人力量
https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/
word2vec参数学习解释
https://arxiv.org/pdf/1411.2738.pdf
Word2Vec教程- Skip-Gram模型,负抽样
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
深度学习和NLP中的注意力机制和记忆力模型
http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/
序列模型
tensorflow.org
使用神经网络进行序列学习
https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
机器学习很有趣第五部分:深度学习的语言翻译和序列的魔力
https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa
如何使用编码器-解码器LSTM来回显随机整数序列
http://machinelearningmastery.com/how-to-use-an-encoder-decoder-lstm-to-echo-sequences-of-random-integers/
tf-seq2seq
https://google.github.io/seq2seq/
Python
机器学习速成课程
https://developers.google.com/machine-learning/crash-course/
令人敬畏的机器学习
https://github.com/josephmisiti/awesome-machine-learning
使用Python掌握机器学习的7个步骤
http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html
一个示例机器学习笔记
http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb
使用Python进行机器学习
https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_quick_guide.htm
实战案例
如何在Python中从头开始实现感知器算法
http://machinelearningmastery.com/implement-perceptron-algorithm-scratch-python/
在Python中使用Scratch实现神经网络
http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/
使用11行代码在Python中实现神经网络
http://iamtrask.github.io/2015/07/12/basic-python-network/
使用Python实现你自己的k-Nearest Neighbor算法
http://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html
来自Scatch的ML
https://github.com/eriklindernoren/ML-From-Scratch
Python机器学习(第2版)代码库
https://github.com/rasbt/python-machine-learning-book-2nd-edition
Scipy和numpy
Scipy讲义
http://www.scipy-lectures.org/
Python Numpy教程
http://cs231n.github.io/python-numpy-tutorial/
Numpy和Scipy简介
https://engineering.ucsb.edu/~shell/che210d/numpy.pdf
Python中的科学家速成课程
http://nbviewer.jupyter.org/gist/rpmuller/5920182
http://nbviewer.jupyter.org/gist/rpmuller/5920182
scikit学习
PyCon scikit-learn教程索引
http://nbviewer.jupyter.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb
scikit-learn分类算法
https://github.com/mmmayo13/scikit-learn-classifiers/blob/master/sklearn-classifiers-tutorial.ipynb
scikit-learn教程
http://scikit-learn.org/stable/tutorial/index.html
简短的scikit-learn教程
https://github.com/mmmayo13/scikit-learn-beginners-tutorials
Tensorflow
Tensorflow教程
https://www.tensorflow.org/tutorials/
TensorFlow简介 - CPU与GPU
https://medium.com/@erikhallstrm/hello-world-tensorflow-649b15aed18c
TensorFlow
https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3
Tensorflow中的RNN
http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/
在TensorFlow中实现CNN进行文本分类
http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
如何使用TensorFlow运行文本摘要
http://pavel.surmenok.com/2016/10/15/how-to-run-text-summarization-with-tensorflow/
PyTorch
PyTorch教程
http://pytorch.org/tutorials/
PyTorch的简单介绍
http://blog.gaurav.im/2017/04/24/a-gentle-intro-to-pytorch/
教程:PyTorch中的深度学习
https://iamtrask.github.io/2017/01/15/pytorch-tutorial/
PyTorch示例
https://github.com/jcjohnson/pytorch-examples
PyTorch教程
https://github.com/MorvanZhou/PyTorch-Tutorial
深度学习研究人员的PyTorch教程
https://github.com/yunjey/pytorch-tutorial
数学
机器学习数学
https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf
机器学习数学
http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf
线性代数
线性代数直观指南
https://betterexplained.com/articles/linear-algebra-guide/
程序员对矩阵乘法的直觉
https://betterexplained.com/articles/matrix-multiplication/
了解Cross产品
https://betterexplained.com/articles/cross-product/
了解Dot产品
https://betterexplained.com/articles/vector-calculus-understanding-the-dot-product/
用于机器学习的线性代数(布法罗大学CSE574)http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/LinearAlgebra.pdf
用于深度学习的线性代数备忘单
https://medium.com/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c
线性代数评论与参考
http://cs229.stanford.edu/section/cs229-linalg.pdf
概率论
用比率理解贝叶斯定理
https://betterexplained.com/articles/understanding-bayes-theorem-with-ratios/
概率论入门
http://cs229.stanford.edu/section/cs229-prob.pdf
机器学习的概率论教程
https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf
概率论(布法罗大学CSE574)
http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/Probability-Theory.pdf
机器学习的概率论(多伦多大学CSC411)
http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial1.pdf
微积分
如何理解导数:商数规则,指数和对数
https://betterexplained.com/articles/how-to-understand-derivatives-the-quotient-rule-exponents-and-logarithms/
如何理解导数:产品,动力和链条规则
(betterexplained.com)
https://betterexplained.com/articles/derivatives-product-power-chain/
矢量微积分:了解渐变
https://betterexplained.com/articles/vector-calculus-understanding-the-gradient/
微分学(斯坦福CS224n)
http://web.stanford.edu/class/cs224n/lecture_notes/cs224n-2017-review-differential-calculus.pdf
微积分概述
http://ml-cheatsheet.readthedocs.io/en/latest/calculus.html
本文由阿里云云栖社区组织翻译。
文章原标题《over-200-of-the-best-machine-learning-nlp-and-python-tutorials-2018-edition》
作者:Robbie Allen
译者:虎说八道,审校:。
end
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