本文多資源，建議閱讀收藏。

本文列出了一系列包含四個主題的相關資源教程列表，一起來充電學習吧~

[ 導讀 ]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！

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瞭解時，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。

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併成為博士生。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

一、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 迴歸（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 卷積神經網絡 Convolutional Neural Networks (CNNs)

1.13 循環神經網絡 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

二、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

三、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

四、數學基礎教程

4.1 線性代數

4.2 概率論

4.3 微積分

一、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
• https://ml.berkeley.edu/blog/2016/12/24/tutorial-2/
• 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 迴歸（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)
• http://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)
• http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

1.12 卷積神經網絡 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 循環神經網絡 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

二、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/

三、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 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 ofPython (iamtrask.github.io)
• http://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-learn Tutorials (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

四、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

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