内容简介：资源 | 机器学习、NLP、Python和Math最好的150余个教程（建议收藏）
编辑 | MingMing
尽管机器学习的历史可以追溯到1959年，但目前，这个领域正以前所未有的速度发展。最近，我一直在网上寻找关于机器学习和NLP各方面的好资源，为了帮助到和我有相同需求的人，我整理了一份迄今为止我发现的最好的教程内容列表。
通过教程中的简介内容讲述一个概念。避免了包括书籍章节涵盖范围广，以及研究论文在教学理念上做的不好的特点。
我把这篇文章分成四个部分：机器学习、NLP、Python和数学。
每个部分中都包含了一些主题文章，但是由于材料巨大，每个部分不可能包含所有可能的主题，我将每个主题限制在5到6个教程中。（由于微信不能插入外链，请点击“阅读原文”查看原文）
机器学习
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Machine Learning is Fun! (medium.com/@ageitgey)
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Machine Learning Crash Course: Part I, Part II, Part III (Machine Learning at Berkeley)
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An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples (toptal.com)
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A Gentle Guide to Machine Learning (monkeylearn.com)
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Which machine learning algorithm should I use? (sas.com)
激活和损失函数
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Sigmoid neurons (neuralnetworksanddeeplearning.com)
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What is the role of the activation function in a neural network? (quora.com)
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Comprehensive list of activation functions in neural networks with pros/cons(stats.stackexchange.com)
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Activation functions and it’s types-Which is better? (medium.com)
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Making Sense of Logarithmic Loss (exegetic.biz)
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Loss Functions (Stanford CS231n)
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L1 vs. L2 Loss function (rishy.github.io)
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The cross-entropy cost function (neuralnetworksanddeeplearning.com)
Bias
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Role of Bias in Neural Networks (stackoverflow.com)
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Bias Nodes in Neural Networks (makeyourownneuralnetwork.blogspot.com)
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What is bias in artificial neural network? (quora.com)
感知器
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Perceptrons (neuralnetworksanddeeplearning.com)
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The Perception (natureofcode.com)
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Single-layer Neural Networks (Perceptrons) (dcu.ie)
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From Perceptrons to Deep Networks (toptal.com)
回归
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Introduction to linear regression analysis (duke.edu)
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Linear Regression (ufldl.stanford.edu)
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Linear Regression (readthedocs.io)
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Logistic Regression (readthedocs.io)
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Simple Linear Regression Tutorial for Machine Learning(machinelearningmastery.com)
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Logistic Regression Tutorial for Machine Learning(machinelearningmastery.com)
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Softmax Regression (ufldl.stanford.edu)
梯度下降算法
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Learning with gradient descent (neuralnetworksanddeeplearning.com)
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Gradient Descent (iamtrask.github.io)
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How to understand Gradient Descent algorithm (kdnuggets.com)
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An overview of gradient descent optimization algorithms(sebastianruder.com)
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Optimization: Stochastic Gradient Descent (Stanford CS231n)
生成式学习
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Generative Learning Algorithms (Stanford CS229)
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A practical explanation of a Naive Bayes classifier (monkeylearn.com)
支持向量机
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An introduction to Support Vector Machines (SVM) (monkeylearn.com)
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Support Vector Machines (Stanford CS229)
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Linear classification: Support Vector Machine, Softmax (Stanford 231n)
反向传播
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Yes you should understand backprop (medium.com/@karpathy)
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Can you give a visual explanation for the back propagation algorithm for neural - networks? (github.com/rasbt)
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How the backpropagation algorithm works(neuralnetworksanddeeplearning.com)
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Backpropagation Through Time and Vanishing Gradients (wildml.com)
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A Gentle Introduction to Backpropagation Through Time(machinelearningmastery.com)
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Backpropagation, Intuitions (Stanford CS231n)
深度学习
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Deep Learning in a Nutshell (nikhilbuduma.com)
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A Tutorial on Deep Learning (Quoc V. Le)
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What is Deep Learning? (machinelearningmastery.com)
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What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep - Learning? (nvidia.com)
优化和降维
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Seven Techniques for Data Dimensionality Reduction (knime.org)
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Principal components analysis (Stanford CS229)
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Dropout: A simple way to improve neural networks (Hinton @ NIPS 2012)
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How to train your Deep Neural Network (rishy.github.io)
长短期记忆网络
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A Gentle Introduction to Long Short-Term Memory Networks by the Experts(machinelearningmastery.com)
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Understanding LSTM Networks (colah.github.io)
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Exploring LSTMs (echen.me)
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Anyone Can Learn To Code an LSTM-RNN in Python (iamtrask.github.io)
卷积神经网络
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Introducing convolutional networks (neuralnetworksanddeeplearning.com)
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Deep Learning and Convolutional Neural Networks(medium.com/@ageitgey)
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Conv Nets: A Modular Perspective (colah.github.io)
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Understanding Convolutions (colah.github.io)
递归神经网络
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Recurrent Neural Networks Tutorial (wildml.com)
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Attention and Augmented Recurrent Neural Networks (distill.pub)
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The Unreasonable Effectiveness of Recurrent Neural Networks(karpathy.github.io)
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A Deep Dive into Recurrent Neural Nets (nikhilbuduma.com)
强化学习
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Simple Beginner’s guide to Reinforcement Learning & its implementation(analyticsvidhya.com)
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A Tutorial for Reinforcement Learning (mst.edu)
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Learning Reinforcement Learning (wildml.com)
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Deep Reinforcement Learning: Pong from Pixels (karpathy.github.io)
生成对抗网络
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What’s a Generative Adversarial Network? (nvidia.com)
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Abusing Generative Adversarial Networks to Make 8-bit Pixel Art(medium.com/@ageitgey)
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An introduction to Generative Adversarial Networks (with code in - TensorFlow) (aylien.com)
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Generative Adversarial Networks for Beginners (oreilly.com)
多任务学习
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An Overview of Multi-Task Learning in Deep Neural Networks(sebastianruder.com)
自然语言处理
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A Primer on Neural Network Models for Natural Language Processing (Yoav Goldberg)
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The Definitive Guide to Natural Language Processing (monkeylearn.com)
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Introduction to Natural Language Processing (algorithmia.com)
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Natural Language Processing Tutorial (vikparuchuri.com)
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Natural Language Processing (almost) from Scratch (arxiv.org)
深入学习和NLP
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Deep Learning applied to NLP (arxiv.org)
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Deep Learning for NLP (without Magic) (Richard Socher)
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Understanding Convolutional Neural Networks for NLP (wildml.com)
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Deep Learning, NLP, and Representations (colah.github.io)
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Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models (explosion.ai)
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Understanding Natural Language with Deep Neural Networks Using Torch(nvidia.com)
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Deep Learning for NLP with Pytorch (pytorich.org)
词向量
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Bag of Words Meets Bags of Popcorn (kaggle.com)
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On word embeddings Part I, Part II, Part III (sebastianruder.com)
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The amazing power of word vectors (acolyer.org)
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word2vec Parameter Learning Explained (arxiv.org)
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Word2Vec Tutorial — The Skip-Gram Model, Negative Sampling(mccormickml.com)
Encoder-Decoder
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Attention and Memory in Deep Learning and NLP (wildml.com)
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Sequence to Sequence Models (tensorflow.org)
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Sequence to Sequence Learning with Neural Networks (NIPS 2014)
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Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences (medium.com/@ageitgey)
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How to use an Encoder-Decoder LSTM to Echo Sequences of Random Integers(machinelearningmastery.com)
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tf-seq2seq (google.github.io)
Python
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7 Steps to Mastering Machine Learning With Python (kdnuggets.com)
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An example machine learning notebook (nbviewer.jupyter.org)
例子
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How To Implement The Perceptron Algorithm From Scratch In Python(machinelearningmastery.com)
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Implementing a Neural Network from Scratch in Python (wildml.com)
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A Neural Network in 11 lines of Python (iamtrask.github.io)
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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)
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How to Learn to Add Numbers with seq2seq Recurrent Neural Networks(machinelearningmastery.com)
Scipy和numpy
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Scipy Lecture Notes (scipy-lectures.org)
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Python Numpy Tutorial (Stanford CS231n)
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An introduction to Numpy and Scipy (UCSB CHE210D)
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A Crash Course in Python for Scientists (nbviewer.jupyter.org)
scikit-learn
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PyCon scikit-learn Tutorial Index (nbviewer.jupyter.org)
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scikit-learn Classification Algorithms (github.com/mmmayo13)
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scikit-learn Tutorials (scikit-learn.org)
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Abridged scikit-learn Tutorials (github.com/mmmayo13)
Tensorflow
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Tensorflow Tutorials (tensorflow.org)
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Introduction to TensorFlow — CPU vs GPU (medium.com/@erikhallstrm)
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TensorFlow: A primer (metaflow.fr)
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RNNs in Tensorflow (wildml.com)
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Implementing a CNN for Text Classification in TensorFlow (wildml.com)
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How to Run Text Summarization with TensorFlow (surmenok.com)
PyTorch
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PyTorch Tutorials (pytorch.org)
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A Gentle Intro to PyTorch (gaurav.im)
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Tutorial: Deep Learning in PyTorch (iamtrask.github.io)
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PyTorch Examples (github.com/jcjohnson)
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PyTorch Tutorial (github.com/MorvanZhou)
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PyTorch Tutorial for Deep Learning Researchers (github.com/yunjey)
数学
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Math for Machine Learning (ucsc.edu)
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Math for Machine Learning (UMIACS CMSC422)
线性代数
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An Intuitive Guide to Linear Algebra (betterexplained.com)
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A Programmer’s Intuition for Matrix Multiplication (betterexplained.com)
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Understanding the Cross Product (betterexplained.com)
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Understanding the Dot Product (betterexplained.com)
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Linear Algebra for Machine Learning (U. of Buffalo CSE574)
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Linear algebra cheat sheet for deep learning (medium.com)
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Linear Algebra Review and Reference (Stanford CS229)
概率
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Understanding Bayes Theorem With Ratios (betterexplained.com)
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Review of Probability Theory (Stanford CS229)
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Probability Theory Review for Machine Learning (Stanford CS229)
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Probability Theory (U. of Buffalo CSE574)
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Probability Theory for Machine Learning (U. of Toronto CSC411)
微积分
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How To Understand Derivatives: The Quotient Rule, Exponents, and Logarithms (betterexplained.com)
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How To Understand Derivatives: The Product, Power & Chain Rules(betterexplained.com)
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Vector Calculus: Understanding the Gradient (betterexplained.com)
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Differential Calculus (Stanford CS224n)
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Calculus Overview (readthedocs.io)
原文链接 https://unsupervisedmethods.com/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78
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