收藏 | 機器學習、NLP、Python和Math最好的150余個教程

盡管機器學習的歷史可以追溯到1959年，但目前，這個領域正以前所未有的速度發展。最近，我一直在網上尋找關于機器學習和NLP各方面的好資源，為了幫助到和我有相同需求的人，我整理了一份迄今為止我發現的最好的教程內容列表。

通過教程中的簡介內容講述一個概念。避免了包括書籍章節涵蓋范圍廣，以及研究論文在教學理念上做的不好的特點。

我把這篇文章分成四個部分：機器學習、NLP、Python和數學。

每個部分中都包含了一些主題文章，但是由于材料巨大，每個部分不可能包含所有可能的主題，我將每個主題限制在5到6個教程中。（由于微信不能插入外鏈，請點擊“閱讀原文”查看原文）
機器學習
    Machine Learning is Fun! (medium.com/@ageitgey)

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

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

    A Gentle Guide to Machine Learning (monkeylearn.com)

    Which machine learning algorithm should I use? (sas.com)

激活和損失函數

    Sigmoid neurons (neuralnetworksanddeeplearning.com)

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

    Comprehensive list of activation functions in neural networks with pros/cons(stats.stackexchange.com)

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

    Making Sense of Logarithmic Loss (exegetic.biz)

    Loss Functions (Stanford CS231n)

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

    The cross-entropy cost function (neuralnetworksanddeeplearning.com)

Bias

    Role of Bias in Neural Networks (stackoverflow.com)

    Bias Nodes in Neural Networks (makeyourownneuralnetwork.blogspot.com)

    What is bias in artificial neural network? (quora.com)

感知器

    Perceptrons (neuralnetworksanddeeplearning.com)

    The Perception (natureofcode.com)

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

    From Perceptrons to Deep Networks (toptal.com)

回歸
    Introduction to linear regression analysis (duke.edu)

    Linear Regression (ufldl.stanford.edu)

    Linear Regression (readthedocs.io)

    Logistic Regression (readthedocs.io)

    Simple Linear Regression Tutorial for Machine Learning(machinelearningmastery.com)

    Logistic Regression Tutorial for Machine Learning(machinelearningmastery.com)

    Softmax Regression (ufldl.stanford.edu)

梯度下降算法

    Learning with gradient descent (neuralnetworksanddeeplearning.com)

    Gradient Descent (iamtrask.github.io)

    How to understand Gradient Descent algorithm (kdnuggets.com)

    An overview of gradient descent optimization algorithms(sebastianruder.com)

    Optimization: Stochastic Gradient Descent (Stanford CS231n)

生成式學習

    Generative Learning Algorithms (Stanford CS229)

    A practical explanation of a Naive Bayes classifier (monkeylearn.com)

支持向量機
    An introduction to Support Vector Machines (SVM) (monkeylearn.com)

    Support Vector Machines (Stanford CS229)

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

反向傳播
    Yes you should understand backprop (medium.com/@karpathy)

    Can you give a visual explanation for the back propagation algorithm for neural - networks? (github.com/rasbt)

    How the backpropagation algorithm works(neuralnetworksanddeeplearning.com)

    Backpropagation Through Time and Vanishing Gradients (wildml.com)

    A Gentle Introduction to Backpropagation Through Time(machinelearningmastery.com)

    Backpropagation, Intuitions (Stanford CS231n)

深度學習
    Deep Learning in a Nutshell (nikhilbuduma.com)

    A Tutorial on Deep Learning (Quoc V. Le)

    What is Deep Learning? (machinelearningmastery.com)

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

優化和降維
    Seven Techniques for Data Dimensionality Reduction (knime.org)

    Principal components analysis (Stanford CS229)

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

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

長短期記憶網絡

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

    Understanding LSTM Networks (colah.github.io)

    Exploring LSTMs (echen.me)

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

卷積神經網絡
    Introducing convolutional networks (neuralnetworksanddeeplearning.com)

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

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

    Understanding Convolutions (colah.github.io)

遞歸神經網絡

    Recurrent Neural Networks Tutorial (wildml.com)

    Attention and Augmented Recurrent Neural Networks (distill.pub)

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

    A Deep Dive into Recurrent Neural Nets (nikhilbuduma.com)

強化學習

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

    A Tutorial for Reinforcement Learning (mst.edu)

    Learning Reinforcement Learning (wildml.com)

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

生成對抗網絡

    What’s a Generative Adversarial Network? (nvidia.com)

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

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

    Generative Adversarial Networks for Beginners (oreilly.com)

多任務學習

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

自然語言處理

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

    The Definitive Guide to Natural Language Processing (monkeylearn.com)

    Introduction to Natural Language Processing (algorithmia.com)

    Natural Language Processing Tutorial (vikparuchuri.com)

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

深入學習和NLP

    Deep Learning applied to NLP (arxiv.org)

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

    Understanding Convolutional Neural Networks for NLP (wildml.com)

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

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

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

    Deep Learning for NLP with Pytorch (pytorich.org)

詞向量

    Bag of Words Meets Bags of Popcorn (kaggle.com)

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

    The amazing power of word vectors (acolyer.org)

    word2vec Parameter Learning Explained (arxiv.org)

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

Encoder-Decoder

    Attention and Memory in Deep Learning and NLP (wildml.com)

    Sequence to Sequence Models (tensorflow.org)

    Sequence to Sequence Learning with Neural Networks (NIPS 2014)

    Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences (medium.com/@ageitgey)

    How to use an Encoder-Decoder LSTM to Echo Sequences of Random Integers(machinelearningmastery.com)

    tf-seq2seq (google.github.io)

Python

    7 Steps to Mastering Machine Learning With Python (kdnuggets.com)

    An example machine learning notebook (nbviewer.jupyter.org)

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

    Implementing a Neural Network from Scratch in Python (wildml.com)

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

    Implementing Your Own k-Nearest Neighbour Algorithm Using Python(kdnuggets.com)
    Demonstration of Memory with a Long Short-Term Memory Network in - Python (machinelearningmastery.com)

    How to Learn to Echo Random Integers with Long Short-Term Memory Recurrent Neural Networks (machinelearningmastery.com)

    How to Learn to Add Numbers with seq2seq Recurrent Neural Networks(machinelearningmastery.com)

Scipy和numpy

    Scipy Lecture Notes (scipy-lectures.org)

    Python Numpy Tutorial (Stanford CS231n)

    An introduction to Numpy and Scipy (UCSB CHE210D)

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

scikit-learn
    PyCon scikit-learn Tutorial Index (nbviewer.jupyter.org)

    scikit-learn Classification Algorithms (github.com/mmmayo13)

    scikit-learn Tutorials (scikit-learn.org)

    Abridged scikit-learn Tutorials (github.com/mmmayo13)

Tensorflow

    Tensorflow Tutorials (tensorflow.org)

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

    TensorFlow: A primer (metaflow.fr)

    RNNs in Tensorflow (wildml.com)

    Implementing a CNN for Text Classification in TensorFlow (wildml.com)

    How to Run Text Summarization with TensorFlow (surmenok.com)

PyTorch

    PyTorch Tutorials (pytorch.org)

    A Gentle Intro to PyTorch (gaurav.im)

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

    PyTorch Examples (github.com/jcjohnson)

    PyTorch Tutorial (github.com/MorvanZhou)

    PyTorch Tutorial for Deep Learning Researchers (github.com/yunjey)

數學
    Math for Machine Learning (ucsc.edu)

    Math for Machine Learning (UMIACS CMSC422)

線性代數
    An Intuitive Guide to Linear Algebra (betterexplained.com)

    A Programmer’s Intuition for Matrix Multiplication (betterexplained.com)

    Understanding the Cross Product (betterexplained.com)

    Understanding the Dot Product (betterexplained.com)

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

    Linear algebra cheat sheet for deep learning (medium.com)

    Linear Algebra Review and Reference (Stanford CS229)

概率
    Understanding Bayes Theorem With Ratios (betterexplained.com)

    Review of Probability Theory (Stanford CS229)

    Probability Theory Review for Machine Learning (Stanford CS229)

    Probability Theory (U. of Buffalo CSE574)

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

微積分
    How To Understand Derivatives: The Quotient Rule, Exponents, and Logarithms (betterexplained.com)

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

    Vector Calculus: Understanding the Gradient (betterexplained.com)

    Differential Calculus (Stanford CS224n)

    Calculus Overview (readthedocs.io)