【2018最新版】 200個機器學習 && NLP && Python 相關教程
近年來,機器學習等新最新技術層出不窮,如何跟蹤最新的熱點以及最新資源,作者Robbie Allen列出了一系列相關資源教程列表,包含四個主題:
機器學習,自然語言處理,Python和數學
,建議大家收藏學習!由於鏈接很多,為大家準備了PDF版,方便學習
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方式一
公眾號後天回復「20180802」
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作者 | Robbie Allen
編譯 | 專知
整理 | Sanglei, Shengsheng
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Over 200 of the Best Machine Learning, NLP, and Python Tutorials?
—?2018 Edition
去年我寫了一份相當受歡迎的博文(在Medium上有16萬閱讀量,相關資源1),列出了我在深入研究大量機器學習資源時發現的最佳教程。十三個月後,現在有許多關於傳統機器學習概念的新教程大量湧現以及過去一年中出現的新技術。圍繞機器學習持續增加的大量內容有著驚人的數量。
本文包含了迄今為止我發現的最好的一些教程內容。它絕不是網上每個ML相關教程的簡單詳盡列表(這個工作量無疑是十分巨大而又枯燥重複的),而是經過詳細篩選後的結果。我的目標就是將我在機器學習和自然語言處理領域各個方面找到的我認為最好的教程整理出來。
在教程中,為了能夠更好的讓讀者理解其中的概念,我將避免羅列書中每章的詳細內容,而是總結一些概念性的介紹內容。為什麼不直接去買本書?當你想要對某些特定的主題或者不同方面進行了初步了解時,我相信這些教程對你可能幫助更大。
本文中我將分四個主題進行整理:
機器學習,自然語言處理,Python和數學
。在每個主題中我將包含一個例子和多個資源。當然我不可能完全覆蓋所有的主題啦。如果你發現我在這裡遺漏了好的教程資源,請聯繫告訴我。為了避免資源重複羅列,我在每個主題下只列出了5、6個教程。下面的每個鏈接都應該鏈接了和其他鏈接不同的資源,也會通過不同的方式(例如幻燈片代碼段)或者不同的角度呈現出這些內容。
相關資源
作者Robbie Allen是以為科技作者和創業者、並自學AI並成為博士生。曾整理許多廣為流傳的機器學習相關資源。
1. 2017版教程資源 Over 150 ofthe Best Machine Learning, NLP, and Python Tutorials I』ve Found(150多個最好的與機器學習,自然語言處理和Python相關的教程)
英文:
https://medium.com/machine-learning-in-practice/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78
中文翻譯:
http://pytlab.org
2. My Curated List of AI and Machine LearningResources from Around the Web( 終極收藏AI領域你不能不關注的大牛、機構、課程、會議、圖書)
英文:
https://medium.com/machine-learning-in-practice/my-curated-list-of-ai-and-machine-learning-resources-from-around-the-web-9a97823b8524
中文翻譯:
http://www.sohu.com/a/168291972_473283
3. Cheat Sheet of Machine Learningand Python (and Math) Cheat Sheets
(值得收藏的27 個機器學習的小抄)
英文:
https://medium.com/machine-learning-in-practice/cheat-sheet-of-machine-learning-and-python-and-math-cheat-sheets-a4afe4e791b6
目錄
1.機器學習
1.1 激活函數與損失函數
1.2 偏差(bias)
1.3 感知機(perceptron)
1.4 回歸(Regression)
1.5 梯度下降(Gradient Descent)
1.6 生成學習(Generative Learning)
1.7 支持向量機(Support Vector Machines)
1.8 反向傳播(Backpropagation)
1.9 深度學習(Deep Learning)
1.10 優化與降維(Optimization and Dimensionality Reduction)
1.11 Long Short Term Memory (LSTM)
1.12 卷積神經網路 Convolutional Neural Networks (CNNs)
1.13 循環神經網路 Recurrent Neural Nets (RNNs)
1.14 強化學習 Reinforcement Learning
1.15 生產對抗模型 Generative Adversarial Networks (GANs)
1.16 多任務學習 Multi-task Learning
2. 自然語言處理 NLP
2.1 深度學習與自然語言處理 Deep Learning and NLP
2.2 詞向量 Word Vectors
2.3 編解碼模型 Encoder-Decoder
3. Python
3.1 樣例 Examples
3.2 Scipy and numpy教程
3.3 scikit-learn教程
3.4 Tensorflow教程
3.5 PyTorch教程
4. 數學基礎教程
4.1 線性代數
4.2 概率論
4.3 微積分
1. 機器學習
Start Here with MachineLearning (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: BestPractices 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
Part III
https://ml.berkeley.edu/blog/2017/02/04/tutorial-3/
An Introduction to MachineLearning Theory and Its Applications: A Visual Tutorial withExamples (toptal.com)
https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer
A Gentle Guide to MachineLearning (monkeylearn.com)
https://monkeylearn.com/blog/a-gentle-guide-to-machine-learning/
Which machine learningalgorithm should I use? (sas.com)
https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
The Machine LearningPrimer (sas.com)
https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/machine-learning-primer-108796.pdf
Machine Learning Tutorial forBeginners (kaggle.com/kanncaa1)
https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners
1.1 激活函數與損失函數
Sigmoidneurons (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#sigmoid_neurons
What is the role of theactivation 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 ofactivation 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 LogarithmicLoss (exegetic.biz)
http://www.exegetic.biz/blog/2015/12/making-sense-logarithmic-loss/
Loss Functions (StanfordCS231n)
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 costfunction (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap3.html#the_cross-entropy_cost_function
1.2 偏差(bias)
Role of Bias in NeuralNetworks (stackoverflow.com)
https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks/2499936#2499936
Bias Nodes in NeuralNetworks (makeyourownneuralnetwork.blogspot.com)
http://makeyourownneuralnetwork.blogspot.com/2016/06/bias-nodes-in-neural-networks.html
What is bias in artificialneural network? (quora.com)
https://www.quora.com/What-is-bias-in-artificial-neural-network
1.3 感知機(perceptron)
Perceptrons (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#perceptrons
The Perception (natureofcode.com)
http://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 DeepNetworks (toptal.com)
https://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks
1.4 回歸(Regression)
Introduction to linearregression analysis (duke.edu)
http://people.duke.edu/~rnau/regintro.htm
LinearRegression (ufldl.stanford.edu)
http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/
LinearRegression (readthedocs.io)
http://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html
Logistic Regression (readthedocs.io)
http://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html
Simple Linear RegressionTutorial for Machine Learning(machinelearningmastery.com)
http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/
Logistic Regression Tutorialfor Machine Learning(machinelearningmastery.com)
http://machinelearningmastery.com/logistic-regression-tutorial-for-machine-learning/
SoftmaxRegression (ufldl.stanford.edu)
http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression/
1.5 梯度下降(Gradient Descent)
Learning with gradientdescent (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap1.html#learning_with_gradient_descent
GradientDescent (iamtrask.github.io)
//iamtrask.github.io/2015/07/27/python-network-part2/
How to understand GradientDescent algorithm (kdnuggets.com)
http://www.kdnuggets.com/2017/04/simple-understand-gradient-descent-algorithm.html
An overview of gradient descentoptimization algorithms(sebastianruder.com)
http://sebastianruder.com/optimizing-gradient-descent/
Optimization: StochasticGradient Descent (Stanford CS231n)
http://cs231n.github.io/optimization-1/
1.6 生成學習(Generative Learning)
Generative LearningAlgorithms (Stanford CS229)
http://cs229.stanford.edu/notes/cs229-notes2.pdf
A practical explanation of aNaive Bayes classifier (monkeylearn.com)
https://monkeylearn.com/blog/practical-explanation-naive-bayes-classifier/
1.7 支持向量機(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 反向傳播(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 backpropagationalgorithm 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/
Backpropagation,Intuitions (Stanford CS231n)
http://cs231n.github.io/optimization-2/
1.9 深度學習(Deep Learning)
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 優化與降維(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/
Understanding LSTMNetworks (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 anLSTM-RNN in Python (iamtrask.github.io)
//iamtrask.github.io/2015/11/15/anyone-can-code-lstm/
1.12 卷積神經網路 Convolutional Neural Networks (CNNs)
Introducing convolutionalnetworks (neuralnetworksanddeeplearning.com)
http://neuralnetworksanddeeplearning.com/chap6.html#introducing_convolutional_networks
Deep Learning and ConvolutionalNeural 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/
UnderstandingConvolutions (colah.github.io)
http://colah.github.io/posts/2014-07-Understanding-Convolutions/
1.13 循環神經網路 Recurrent Neural Nets (RNNs)
Recurrent Neural NetworksTutorial (wildml.com)
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
Attention and AugmentedRecurrent Neural Networks (distill.pub)
http://distill.pub/2016/augmented-rnns/
The Unreasonable Effectivenessof Recurrent Neural Networks(karpathy.github.io)
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
A Deep Dive into RecurrentNeural Nets (nikhilbuduma.com)
http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/
1.14 強化學習 Reinforcement Learning
Simple Beginner』s guide toReinforcement Learning & its implementation(analyticsvidhya.com)
https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/
A Tutorial for ReinforcementLearning (mst.edu)
https://web.mst.edu/~gosavia/tutorial.pdf
Learning ReinforcementLearning (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/
1.15 生產對抗模型 Generative Adversarial Networks (GANs)
Adversarial MachineLearning (aaai18adversarial.github.io)
https://aaai18adversarial.github.io/slides/AML.pptx
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/
Generative Adversarial Networksfor Beginners (oreilly.com)
https://www.oreilly.com/learning/generative-adversarial-networks-for-beginners
1.16 多任務學習 Multi-task Learning
An Overview of Multi-TaskLearning in Deep Neural Networks(sebastianruder.com)
http://sebastianruder.com/multi-task/index.html
2. 自然語言處理 NLP
Natural Language Processing isFun! (medium.com/@ageitgey)
https://medium.com/@ageitgey/natural-language-processing-is-fun-9a0bff37854e
A Primer on Neural NetworkModels for Natural LanguageProcessing (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 深度學習與自然語言處理 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 詞向量 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/
word2vec Parameter LearningExplained (arxiv.org)
https://arxiv.org/pdf/1411.2738.pdf
Word2Vec Tutorial—TheSkip-Gram Model, Negative Sampling(mccormickml.com)
http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
2.3 編解碼模型 Encoder-Decoder
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/
tf-seq2seq (google.github.io)
https://google.github.io/seq2seq/
3. Python
Machine Learning CrashCourse (google.com)
https://developers.google.com/machine-learning/crash-course/
Awesome MachineLearning (github.com/josephmisiti)
https://github.com/josephmisiti/awesome-machine-learning#python
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
Machine Learning withPython (tutorialspoint.com)
https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_quick_guide.htm
3.1 樣例 Examples
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)
//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
Python Machine Learning (2ndEd.) Code Repository (github.com/rasbt)
https://github.com/rasbt/python-machine-learning-book-2nd-edition
3.2 Scipy and numpy教程
Scipy LectureNotes (scipy-lectures.org)
http://www.scipy-lectures.org/
Python NumpyTutorial (Stanford CS231n)
http://cs231n.github.io/python-numpy-tutorial/
An introduction to Numpy andScipy (UCSB CHE210D)
https://engineering.ucsb.edu/~shell/che210d/numpy.pdf
A Crash Course in Python forScientists (nbviewer.jupyter.org)
http://nbviewer.jupyter.org/gist/rpmuller/5920182#ii.-numpy-and-scipy
3.3 scikit-learn教程
PyCon scikit-learn TutorialIndex (nbviewer.jupyter.org)
http://nbviewer.jupyter.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb
scikit-learn ClassificationAlgorithms (github.com/mmmayo13)
https://github.com/mmmayo13/scikit-learn-classifiers/blob/master/sklearn-classifiers-tutorial.ipynb
scikit-learnTutorials (scikit-learn.org)
http://scikit-learn.org/stable/tutorial/index.html
Abridged scikit-learnTutorials (github.com/mmmayo13)
https://github.com/mmmayo13/scikit-learn-beginners-tutorials
3.4 Tensorflow教程
Tensorflow Tutorials (tensorflow.org)
https://www.tensorflow.org/tutorials/
Introduction to TensorFlow—CPUvs GPU (medium.com/@erikhallstrm)
https://medium.com/@erikhallstrm/hello-world-tensorflow-649b15aed18c
TensorFlow: Aprimer (metaflow.fr)
https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3
RNNs inTensorflow (wildml.com)
http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/
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/
3.5 PyTorch教程
PyTorchTutorials (pytorch.org)
http://pytorch.org/tutorials/
A Gentle Intro toPyTorch (gaurav.im)
http://blog.gaurav.im/2017/04/24/a-gentle-intro-to-pytorch/
Tutorial: Deep Learning inPyTorch (iamtrask.github.io)
https://iamtrask.github.io/2017/01/15/pytorch-tutorial/
PyTorch Examples (github.com/jcjohnson)
https://github.com/jcjohnson/pytorch-examples
PyTorchTutorial (github.com/MorvanZhou)
https://github.com/MorvanZhou/PyTorch-Tutorial
PyTorch Tutorial for DeepLearning Researchers (github.com/yunjey)
https://github.com/yunjey/pytorch-tutorial
4. 數學基礎教程
Math for MachineLearning (ucsc.edu)
https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf
Math for MachineLearning (UMIACS CMSC422)
http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf
4.1 線性代數
An Intuitive Guide to LinearAlgebra (betterexplained.com)
https://betterexplained.com/articles/linear-algebra-guide/
A Programmer』s Intuition forMatrix Multiplication (betterexplained.com)
https://betterexplained.com/articles/matrix-multiplication/
Understanding the Cross Product (betterexplained.com)
https://betterexplained.com/articles/cross-product/
Understanding the DotProduct (betterexplained.com)
https://betterexplained.com/articles/vector-calculus-understanding-the-dot-product/
Linear Algebra for MachineLearning (U. of Buffalo CSE574)
http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/LinearAlgebra.pdf
Linear algebra cheat sheet fordeep learning (medium.com)
https://medium.com/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c
Linear Algebra Review andReference (Stanford CS229)
http://cs229.stanford.edu/section/cs229-linalg.pdf
4.2 概率論
Understanding Bayes TheoremWith Ratios (betterexplained.com)
https://betterexplained.com/articles/understanding-bayes-theorem-with-ratios/
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: Understandingthe Gradient (betterexplained.com)
https://betterexplained.com/articles/vector-calculus-understanding-the-gradient/
DifferentialCalculus (Stanford CS224n)
http://web.stanford.edu/class/cs224n/lecture_notes/cs224n-2017-review-differential-calculus.pdf
CalculusOverview (readthedocs.io)
http://ml-cheatsheet.readthedocs.io/en/latest/calculus.html
原文鏈接:
https://medium.com/machine-learning-in-practice/over-200-of-the-best-machine-learning-nlp-and-python-tutorials-2018-edition-dd8cf53cb7dc