# 机器学习、NLP、Python、数学。。最全的AI学习资源都在这了！

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﻿ ## 1.机器学习及相关内容

1.1 机器学习

Start Here with Machine Learning (machinelearningmastery.com)

Machine Learning is Fun! (medium.com/@ageitgey)

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

Machine Learning Crash Course: Part IPart IIPart III (Machine Learning at Berkeley)

A Gentle Guide to Machine Learning (monkeylearn.com)

The Machine Learning Primer (sas.com)

Machine Learning Tutorial for Beginners (kaggle.com/kanncaa1)

1.2 激活函数与损失函数

Sigmoid neurons (neuralnetworksanddeeplearning.com)

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

1.3 偏差

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)

1.4 感知

Perceptrons (neuralnetworksanddeeplearning.com)

The Perception (natureofcode.com)

From Perceptrons to Deep Networks (toptal.com)

1.5 回归

Linear Regression (ufldl.stanford.edu)

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

Logistic Regression Tutorial for Machine Learning(machinelearningmastery.com)

Softmax Regression (ufldl.stanford.edu)

1.6 梯度下降

How to understand Gradient Descent algorithm (kdnuggets.com)

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

Optimization: Stochastic Gradient Descent (Stanford CS231n)

1.7 生成学习

Generative Learning Algorithms (Stanford CS229)

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

1.8 支持向量机

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

Support Vector Machines (Stanford CS229)

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

1.9 反向传播

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

How the backpropagation algorithm works(neuralnetworksanddeeplearning.com)

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

Backpropagation, Intuitions (Stanford CS231n)

1.10 深度学习

A Guide to Deep Learning by YN² (yerevann.com)

Deep Learning in a Nutshell (nikhilbuduma.com)

A Tutorial on Deep Learning (Quoc V. Le)

What is Deep Learning? (machinelearningmastery.com)

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

1.11 优化方法与降维方法

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)

1.12 LSTM

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)

1.13 CNN

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)

1.14 RNN

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)

1.15 强化学习 RL

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

Learning Reinforcement Learning (wildml.com)

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

1.16 生成对抗网络 GANs

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

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

Generative Adversarial Networks for Beginners (oreilly.com)

1.17 多任务学习

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

## 2.NLP及相关内容

2.1 NLP

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

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

Introduction to Natural Language Processing (algorithmia.com)

Natural Language Processing Tutorial (vikparuchuri.com)

2.2 深度学习与 NLP

Deep Learning applied to NLP (arxiv.org)

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

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

Deep Learning for NLP with Pytorch (pytorich.org)

2.3 词向量

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

On word embeddings Part IPart IIPart III (sebastianruder.com)

The amazing power of word vectors (acolyer.org)

word2vec Parameter Learning Explained (arxiv.org)

Word2Vec Tutorial — The Skip-Gram ModelNegative Sampling(mccormickml.com)

2.4 编码-解码

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

Sequence to Sequence Models (tensorflow.org)

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

## 3.Python 及相关内容

3.1示例

Awesome Machine Learning (github.com/josephmisiti)

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

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

Machine Learning with Python (tutorialspoint.com)

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

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

ML from Scatch (github.com/eriklindernoren)

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

3.2 函数与 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)

3.3 算法库

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)

3.4 TensorFlow

Tensorflow Tutorials (tensorflow.org)

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

TensorFlow: A primer (metaflow.fr)

RNNs in Tensorflow (wildml.com)

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

3.5 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)

## 4.数学及相关内容

4.1 机器学习中的数学

Math for Machine Learning (ucsc.edu)

Math for Machine Learning (UMIACS CMSC422)

4.2 线性代数

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)

4.3 概率论

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)

4.4 微积分

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)