這篇文章包含了我目前為止找到的最好的教程內(nèi)容。這不是一張羅列了所有網(wǎng)上跟機(jī)器學(xué)習(xí)相關(guān)教程的清單——不然就太冗長(zhǎng)太重復(fù)了,。我這里并沒(méi)有包括那些質(zhì)量一般的內(nèi)容,。我的目標(biāo)是把能找到的最好的教程與機(jī)器學(xué)習(xí)和自然語(yǔ)言處理的延伸主題們連接到一起。

　　我這里指的“教程”,，是指那些為了簡(jiǎn)潔地傳授一個(gè)概念而寫(xiě)的介紹性內(nèi)容,。我盡量避免了教科書(shū)里的章節(jié)，因?yàn)樗鼈兒w了更廣的內(nèi)容,，或者是研究論文,，通常對(duì)于傳授概念來(lái)說(shuō)并不是很有幫助,。如果是那樣的話，為何不直接買(mǎi)書(shū)呢?當(dāng)你想要學(xué)習(xí)一個(gè)基本主題或者是想要獲得更多觀點(diǎn)的時(shí)候,，教程往往很有用,。

　　我把這篇文章分為了四個(gè)部分：機(jī)器學(xué)習(xí)，自然語(yǔ)言處理,，python和數(shù)學(xué),。在每個(gè)部分中我都列舉了一些主題，但是因?yàn)椴牧系臄?shù)量龐大,，我不可能涉及到每一個(gè)主題,。

　　如果你發(fā)現(xiàn)到我遺漏了哪些好的教程，請(qǐng)告訴我!我盡量把每個(gè)主題下的教程控制在五個(gè)或者六個(gè),，如果超過(guò)了這個(gè)數(shù)字就難免會(huì)有重復(fù),。每一個(gè)鏈接都包含了與其他鏈接不同的材料，或使用了不同的方式表達(dá)信息(例如：使用代碼,，幻燈片和長(zhǎng)文),，或者是來(lái)自不同的角度。成都加米谷大數(shù)據(jù)培訓(xùn),，小班教學(xué),。

　　機(jī)器學(xué)習(xí)

　　Start Here with Machine Learning (machinelearningmastery)

　　machinelearningmastery/start-here/

　　Machine Learning is Fun! (medium/@ageitgey)

　　medium/@ageitgey/machine-learning-is-fun-80ea3ec3c471

　　Rules of Machine Learning: Best Practices for ML Engineering(martin.zinkevich)

　　martin.zinkevich/rules_of_ml/rules_of_ml.pdf

　　Machine Learning Crash Course: Part I, Part II, Part III (Machine Learning at Berkeley)

　　ml.berkeley/blog/2022/11/06/tutorial-1/

　　ml.berkeley/blog/2022/12/24/tutorial-2/

　　ml.berkeley/blog/2022/02/04/tutorial-3/

　　An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples (toptal)

　　toptal/machine-learning/machine-learning-theory-an-introductory-primer

　　A Gentle Guide to Machine Learning (monkeylearn)

　　monkeylearn/blog/gentle-guide-to-machine-learning/

　　Which machine learning algorithm should I use? (sas)

　　blogs.sas/content/subconsciousmusings/2022/04/12/machine-learning-algorithm-use/

　　The Machine Learning Primer (sas)

　　sas/content/dam/SAS/en_us/doc/whitepaper1/machine-learning-primer-108796.pdf

　　Machine Learning Tutorial for Beginners (kaggle/kanncaa1)

　　kaggle/kanncaa1/machine-learning-tutorial-for-beginners

　　激活和損失函數(shù)

　　Sigmoid neurons (neuralnetworksanddeeplearning)

　　neuralnetworksanddeeplearning/chap1.html#sigmoid_neurons

　　What is the role of the activation function in a neural network? (quora)

　　quora/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)

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

　　Activation functions and it’s types-Which is better? (medium)

　　medium/towards-data-science/activation-functions-and-its-types-which-is-better-a9a5310cc8f

　　Making Sense of Logarithmic Loss (exegetic)

　　exegetic/blog/2022/12/making-sense-logarithmic-loss/

　　Loss Functions (Stanford CS231n)

　　cs231n.github/neural-networks-2/#losses

　　L1 vs. L2 Loss function (rishy.github.io)

　　rishy.github/ml/2022/07/28/l1-vs-l2-loss/

　　The cross-entropy cost function (neuralnetworksanddeeplearning)

　　neuralnetworksanddeeplearning/chap3.html#the_cross-entropy_cost_function

　　偏差

　　Role of Bias in Neural Networks (stackoverflow)

　　stackoverflow/questions/2480650/role-of-bias-in-neural-networks/2499936#2499936

　　Bias Nodes in Neural Networks(makeyourownneuralnetwork.blogspot)

　　makeyourownneuralnetwork.blogspot/2022/06/bias-nodes-in-neural-networks.html

　　What is bias in artificial neural network? (quora)

　　quora/What-is-bias-in-artificial-neural-network

　　感知機(jī)

　　Perceptrons (neuralnetworksanddeeplearning)

　　neuralnetworksanddeeplearning/chap1.html#perceptrons

　　The Perception (natureofcode)

　　natureofcode/book/chapter-10-neural-networks/#chapter10_figure3

　　Single-layer Neural Networks (Perceptrons) (dcu.ie)

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

　　From Perceptrons to Deep Networks (toptal)

　　toptal/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

　　回歸

　　Introduction to linear regression analysis (duke.edu)

　　people.duke/~rnau/regintro.htm

　　Linear Regression (ufldl.stanford.edu)

　　ufldl.stanford/tutorial/supervised/LinearRegression/

　　Linear Regression (readthedocs.io)

　　ml-cheatsheet.readthedocs/en/latest/linear_regression.html

　　Logistic Regression (readthedocs.io)

　　ml-cheatsheet.readthedocs/en/latest/logistic_regression.html

　　Simple Linear Regression Tutorial for Machine Learning(machinelearningmastery)

　　machinelearningmastery/simple-linear-regression-tutorial-for-machine-learning/

　　Logistic Regression Tutorial for Machine Learning(machinelearningmastery)

　　machinelearningmastery/logistic-regression-tutorial-for-machine-learning/

　　Softmax Regression (ufldl.stanford.edu)

　　ufldl.stanford/tutorial/supervised/SoftmaxRegression/

　　梯度下降

　　Learning with gradient descent (neuralnetworksanddeeplearning)

　　neuralnetworksanddeeplearning/chap1.html#learning_with_gradient_descent

　　Gradient Descent (iamtrask.github.io)

　　iamtrask.github/2022/07/27/python-network-part2/

　　How to understand Gradient Descent algorithm (kdnuggets)

　　kdnuggets/2022/04/simple-understand-gradient-descent-algorithm.html

　　An overview of gradient descent optimization algorithms(sebastianruder)

　　sebastianruder/optimizing-gradient-descent/

　　Optimization: Stochastic Gradient Descent (Stanford CS231n)

　　cs231n.github/optimization-1/

　　生成學(xué)習(xí)

　　Generative Learning Algorithms (Stanford CS229)

　　cs229.stanford/notes/cs229-notes2.pdf

　　A practical explanation of a Naive Bayes classifier (monkeylearn)

　　monkeylearn/blog/practical-explanation-naive-bayes-classifier/

　　支持向量機(jī)

　　An introduction to Support Vector Machines (SVM) (monkeylearn)

　　monkeylearn/blog/introduction-to-support-vector-machines-svm/

　　Support Vector Machines (Stanford CS229)

　　cs229.stanford/notes/cs229-notes3.pdf

　　Linear classification: Support Vector Machine, Softmax (Stanford 231n)

　　cs231n.github/linear-classify/

　　深度學(xué)習(xí)

　　A Guide to Deep Learning by YN2 (yerevann)

　　yerevann/a-guide-to-deep-learning/

　　Deep Learning Papers Reading Roadmap (github/floodsung)

　　github/floodsung/Deep-Learning-Papers-Reading-Roadmap

　　Deep Learning in a Nutshell (nikhilbuduma)

　　nikhilbuduma/2014/12/29/deep-learning-in-a-nutshell/

　　A Tutorial on Deep Learning (Quoc V. Le)

　　ai.stanford/~quocle/tutorial1.pdf

　　What is Deep Learning? (machinelearningmastery)

　　machinelearningmastery/what-is-deep-learning/

　　What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? (nvidia)

　　blogs.nvidia/blog/2022/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

　　Deep Learning?—?The Straight Dope (gluon.mxnet.io)

　　gluon.mxnet/

　　優(yōu)化和降維

　　Seven Techniques for Data Dimensionality Reduction (knime)

　　knime/blog/seven-techniques-for-data-dimensionality-reduction

　　Principal components analysis (Stanford CS229)

　　cs229.stanford/notes/cs229-notes10.pdf

　　Dropout: A simple way to improve neural networks (Hinton @ NIPS 2012)

　　cs229.stanford/notes/cs229-notes10.pdf

　　How to train your Deep Neural Network (rishy.github.io)

　　rishy.github/ml/2022/01/05/how-to-train-your-dnn/

　　長(zhǎng)短期記憶(LSTM)

　　A Gentle Introduction to Long Short-Term Memory Networks by the Experts(machinelearningmastery)

　　machinelearningmastery/gentle-introduction-long-short-term-memory-networks-experts/

　　Understanding LSTM Networks (colah.github.io)

　　colah.github/posts/2022-08-Understanding-LSTMs/

　　Exploring LSTMs (echen.me)

　　blog.echen/2022/05/30/exploring-lstms/

　　Anyone Can Learn To Code an LSTM-RNN in Python (iamtrask.github.io)

　　iamtrask.github/2022/11/15/anyone-can-code-lstm/

　　卷積神經(jīng)網(wǎng)絡(luò)

　　Introducing convolutional networks (neuralnetworksanddeeplearning)

　　neuralnetworksanddeeplearning/chap6.html#introducing_convolutional_networks

　　Deep Learning and Convolutional Neural Networks(medium/@ageitgey)

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

　　Conv Nets: A Modular Perspective (colah.github.io)

　　colah.github/posts/2014-07-Conv-Nets-Modular/

　　Understanding Convolutions (colah.github.io)

　　colah.github/posts/2014-07-Understanding-Convolutions/

　　遞歸神經(jīng)網(wǎng)絡(luò)

　　Recurrent Neural Networks Tutorial (wildml)

　　wildml/2022/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/

　　Attention and Augmented Recurrent Neural Networks (distill)

　　distill/2022/augmented-rnns/

　　The Unreasonable Effectiveness of Recurrent Neural Networks(karpathy.github.io)

　　karpathy.github/2022/05/21/rnn-effectiveness/

　　A Deep Dive into Recurrent Neural Nets (nikhilbuduma)

　　nikhilbuduma/2022/01/11/a-deep-dive-into-recurrent-neural-networks/

　　強(qiáng)化學(xué)習(xí)

　　Simple Beginner’s guide to Reinforcement Learning & its implementation(analyticsvidhya)

　　analyticsvidhya/blog/2022/01/introduction-to-reinforcement-learning-implementation/

　　A Tutorial for Reinforcement Learning (mst.edu)

　　web.mst/~gosavia/tutorial.pdf

　　Learning Reinforcement Learning (wildml)

　　wildml/2022/10/learning-reinforcement-learning/

　　Deep Reinforcement Learning: Pong from Pixels (karpathy.github.io)

　　karpathy.github/2022/05/31/rl/

　　生成對(duì)抗網(wǎng)絡(luò)(GANs)

　　Adversarial Machine Learning (aaai18adversarial.github.io)

　　aaai18adversarial.github/slides/AML.pptx

　　What’s a Generative Adversarial Network? (nvidia)

　　blogs.nvidia/blog/2022/05/17/generative-adversarial-network/

　　Abusing Generative Adversarial Networks to Make 8-bit Pixel Art(medium/@ageitgey)

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

　　An introduction to Generative Adversarial Networks (with code in TensorFlow) (aylien)

　　blog.aylien/introduction-generative-adversarial-networks-code-tensorflow/

　　Generative Adversarial Networks for Beginners (oreilly)

　　oreilly/learning/generative-adversarial-networks-for-beginners

　　多任務(wù)學(xué)習(xí)

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

　　sebastianruder/multi-task/index.html

　　自然語(yǔ)言處理

　　Natural Language Processing is Fun! (medium/@ageitgey)

　　medium/@ageitgey/natural-language-processing-is-fun-9a0bff37854e

　　A Primer on Neural Network Models for Natural Language Processing(Yoav Goldberg)

　　u.cs.biu.ac.il/~yogo/nnlp.pdf

　　The Definitive Guide to Natural Language Processing (monkeylearn)

　　monkeylearn/blog/the-definitive-guide-to-natural-language-processing/

　　Introduction to Natural Language Processing (algorithmia)

　　blog.algorithmia/introduction-natural-language-processing-nlp/

　　Natural Language Processing Tutorial (vikparuchuri)

　　vikparuchuri/blog/natural-language-processing-tutorial/

　　Natural Language Processing (almost) from Scratch (arxiv)

　　arxiv/pdf/1103.0398.pdf

　　深度學(xué)習(xí)和學(xué)歷證自然語(yǔ)言處理

　　Deep Learning applied to NLP (arxiv)

　　arxiv/pdf/1703.03091.pdf

　　Deep Learning for NLP (without Magic) (Richard Socher)

　　nlp.stanford/courses/NAACL2013/NAACL2013-Socher-Manning-DeepLearning.pdf

　　Understanding Convolutional Neural Networks for NLP (wildml)

　　wildml/2022/11/understanding-convolutional-neural-networks-for-nlp/

　　Deep Learning, NLP, and Representations (colah.github.io)

　　colah.github/posts/2014-07-NLP-RNNs-Representations/

　　Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models (explosion.ai)

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

　　Understanding Natural Language with Deep Neural Networks Using Torch(nvidia)

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

　　Deep Learning for NLP with Pytorch (pytorich)

　　pytorch/tutorials/beginner/deep_learning_nlp_tutorial.html

　　詞向量

　　Bag of Words Meets Bags of Popcorn (kaggle)

　　kaggle/c/word2vec-nlp-tutorial

　　On word embeddings Part I, Part II, Part III (sebastianruder)

　　sebastianruder/word-embeddings-1/index.html

　　sebastianruder/word-embeddings-softmax/index.html

　　sebastianruder/secret-word2vec/index.html

　　The amazing power of word vectors (acolyer)

　　blog.acolyer/2022/04/21/the-amazing-power-of-word-vectors/

　　word2vec Parameter Learning Explained (arxiv)

　　arxiv/pdf/1411.2738.pdf

　　Word2Vec Tutorial?—?The Skip-Gram Model, Negative Sampling(mccormickml)

　　mccormickml/2022/04/19/word2vec-tutorial-the-skip-gram-model/

　　mccormickml/2022/01/11/word2vec-tutorial-part-2-negative-sampling/

　　編碼器-解碼器

　　Attention and Memory in Deep Learning and NLP (wildml)

　　wildml/2022/01/attention-and-memory-in-deep-learning-and-nlp/

　　Sequence to Sequence Models (tensorflow)

　　tensorflow/tutorials/seq2seq

　　Sequence to Sequence Learning with Neural Networks (NIPS 2014)

　　papers.nips/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/@ageitgey)

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

　　tf-seq2seq (google.github.io)

　　google.github/seq2seq/

　　Python

　　Machine Learning Crash Course (google)

　　developers.google/machine-learning/crash-course/

　　Awesome Machine Learning (github/josephmisiti)

　　github/josephmisiti/awesome-machine-learning#python

　　7 Steps to Mastering Machine Learning With Python (kdnuggets)

　　kdnuggets/2022/11/seven-steps-machine-learning-python.html

　　An example machine learning notebook (nbviewer.jupyter)

　　nbviewer.jupyter/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb

　　Machine Learning with Python (tutorialspoint)

　　tutorialspoint/machine_learning_with_python/machine_learning_with_python_quick_guide.htm

　　范例

　　How To Implement The Perceptron Algorithm From Scratch In Python(machinelearningmastery)

　　machinelearningmastery/implement-perceptron-algorithm-scratch-python/

　　Implementing a Neural Network from Scratch in Python (wildml)

　　wildml/2022/09/implementing-a-neural-network-from-scratch/

　　A Neural Network in 11 lines of Python (iamtrask.github.io)

　　iamtrask.github/2022/07/12/basic-python-network/

　　Implementing Your Own k-Nearest Neighbour Algorithm Using Python(kdnuggets)

　　kdnuggets/2022/01/implementing-your-own-knn-using-python.html

　　ML from Scatch (github/eriklindernoren)

　　github/eriklindernoren/ML-From-Scratch

　　Python Machine Learning (2nd Ed.) Code Repository (github/rasbt)

　　github/rasbt/python-machine-learning-book-2nd-edition

　　Scipy and numpy

　　Scipy Lecture Notes (scipy-lectures)

　　scipy-lectures/

　　Python Numpy Tutorial (Stanford CS231n)

　　cs231n.github/python-numpy-tutorial/

　　An introduction to Numpy and Scipy (UCSB CHE210D)

　　engineering.ucsb/~shell/che210d/numpy.pdf

　　A Crash Course in Python for Scientists (nbviewer.jupyter)

　　nbviewer.jupyter/gist/rpmuller/5920222#ii.-numpy-and-scipy

　　scikit-learn

　　PyCon scikit-learn Tutorial Index (nbviewer.jupyter)

　　nbviewer.jupyter/github/jakevdp/sklearn_pycon2022/blob/master/notebooks/Index.ipynb

　　scikit-learn Classification Algorithms (github/mmmayo13)

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

　　scikit-learn Tutorials (scikit-learn)

　　scikit-learn/stable/tutorial/index.html

　　Abridged scikit-learn Tutorials (github/mmmayo13)

　　github/mmmayo13/scikit-learn-beginners-tutorials

　　Tensorflow

　　Tensorflow Tutorials (tensorflow)

　　tensorflow/tutorials/

　　Introduction to TensorFlow?—?CPU vs GPU (medium/@erikhallstrm)

　　medium/@erikhallstrm/hello-world-tensorflow-649b15aed18c

　　TensorFlow: A primer (metaflow.fr)

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

　　RNNs in Tensorflow (wildml)

　　wildml/2022/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

　　Implementing a CNN for Text Classification in TensorFlow (wildml)

　　wildml/2022/12/implementing-a-cnn-for-text-classification-in-tensorflow/

　　How to Run Text Summarization with TensorFlow (surmenok)

　　pavel.surmenok/2022/10/15/how-to-run-text-summarization-with-tensorflow/

　　PyTorch

　　PyTorch Tutorials (pytorch)

　　pytorch/tutorials/

　　A Gentle Intro to PyTorch (gaurav)

　　blog.gaurav/2022/04/24/a-gentle-intro-to-pytorch/

　　Tutorial: Deep Learning in PyTorch (iamtrask.github.io)

　　iamtrask.github/2022/01/15/pytorch-tutorial/

　　PyTorch Examples (github/jcjohnson)

　　github/jcjohnson/pytorch-examples

　　PyTorch Tutorial (github/MorvanZhou)

　　github/MorvanZhou/PyTorch-Tutorial

　　PyTorch Tutorial for Deep Learning Researchers (github/yunjey)

　　github/yunjey/pytorch-tutorial

　　數(shù)學(xué)

　　Math for Machine Learning (ucsc.edu)

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

　　Math for Machine Learning (UMIACS CMSC422)

　　umiacs.umd/~hal/courses/2013S_ML/math4ml.pdf

　　線性代數(shù)

　　An Intuitive Guide to Linear Algebra (betterexplained)

　　betterexplained/articles/linear-algebra-guide/

　　A Programmer’s Intuition for Matrix Multiplication (betterexplained)

　　betterexplained/articles/matrix-multiplication/

　　Understanding the Cross Product (betterexplained)

　　betterexplained/articles/cross-product/

　　Understanding the Dot Product (betterexplained)

　　betterexplained/articles/vector-calculus-understanding-the-dot-product/

　　Linear Algebra for Machine Learning (U. of Buffalo CSE574)

　　cedar.buffalo/~srihari/CSE574/Chap1/LinearAlgebra.pdf

　　Linear algebra cheat sheet for deep learning (medium)

　　medium/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c

　　Linear Algebra Review and Reference (Stanford CS229)

　　cs229.stanford/section/cs229-linalg.pdf

　　概率

　　Understanding Bayes Theorem With Ratios (betterexplained)

　　betterexplained/articles/understanding-bayes-theorem-with-ratios/

　　Review of Probability Theory (Stanford CS229)

　　cs229.stanford/section/cs229-prob.pdf

　　Probability Theory Review for Machine Learning (Stanford CS229)

　　see.stanford/materials/aimlcs229/cs229-prob.pdf

　　Probability Theory (U. of Buffalo CSE574)

　　cedar.buffalo/~srihari/CSE574/Chap1/Probability-Theory.pdf

　　Probability Theory for Machine Learning (U. of Toronto CSC411)

　　cs.toronto/~urtasun/courses/CSC411_Fall16/tutorial1.pdf

　　微積分

　　How To Understand Derivatives: The Quotient Rule, Exponents, and Logarithms (betterexplained)

　　betterexplained/articles/how-to-understand-derivatives-the-quotient-rule-exponents-and-logarithms/

　　How To Understand Derivatives: The Product, Power & Chain Rules(betterexplained)

　　betterexplained/articles/derivatives-product-power-chain/

　　Vector Calculus: Understanding the Gradient (betterexplained)

　　betterexplained/articles/vector-calculus-understanding-the-gradient/

　　Differential Calculus (Stanford CS224n)

　　web.stanford/class/cs224n/lecture_notes/cs224n-2022-review-differential-calculus.pdf

　　Calculus Overview (readthedocs.io)

　　ml-cheatsheet.readthedocs/en/latest/calculus.html