超过 150 个最佳机器学习，NLP 和 Python教程

摘要：作者微信号微信公众号简书地址我把这篇文章分为四个部分机器学习，，和数学。在这篇文章中，我把每个主题的教程数量都是控制在五到六个，这些精选出来的教程都是非常重要的。每一个链接都会链接到别的链接，从而导致很多新的教程。

作者：chen_h
微信号 & QQ：862251340
微信公众号：coderpai
简书地址：http://www.jianshu.com/p/2be3...

我把这篇文章分为四个部分：机器学习，NLP，Python 和 数学。我在每一部分都会包含一些关键主题，但是网上资料太广泛了，所以我不可能包括每一个可能的主题。

如果你发现好的教程，请告诉我。在这篇文章中，我把每个主题的教程数量都是控制在五到六个，这些精选出来的教程都是非常重要的。每一个链接都会链接到别的链接，从而导致很多新的教程。

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)

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]12

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)

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)

Perceptrons (neuralnetworksanddeeplearning.com)

The Perception (natureofcode.com)

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

From Perceptrons to Deep Networks (toptal.com)

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]29

[Logistic Regression Tutorial for Machine Learning]30

Softmax Regression (ufldl.stanford.edu)

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]35

Optimization: Stochastic Gradient Descent (Stanford CS231n)

Generative Learning Algorithms (Stanford CS229)

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

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

Support Vector Machines (Stanford CS229)

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

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]45

Backpropagation Through Time and Vanishing Gradients (wildml.com)

[A Gentle Introduction to Backpropagation Through Time]47

Backpropagation, Intuitions (Stanford CS231n)

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)

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)

[A Gentle Introduction to Long Short-Term Memory Networks by the Experts]57

Understanding LSTM Networks (colah.github.io)

Exploring LSTMs (echen.me)

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

Introducing convolutional networks (neuralnetworksanddeeplearning.com)

[Deep Learning and Convolutional Neural Networks]62

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

Understanding Convolutions (colah.github.io)

Recurrent Neural Networks Tutorial (wildml.com)

Attention and Augmented Recurrent Neural Networks (distill.pub)

[The Unreasonable Effectiveness of Recurrent Neural Networks]68

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

[Simple Beginner’s guide to Reinforcement Learning & its implementation]70

A Tutorial for Reinforcement Learning (mst.edu)

Learning Reinforcement Learning (wildml.com)

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

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

[Abusing Generative Adversarial Networks to Make 8-bit Pixel Art]75

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

Generative Adversarial Networks for Beginners (oreilly.com)

[An Overview of Multi-Task Learning in Deep Neural Networks]78

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)

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]89

Deep Learning for NLP with Pytorch (pytorich.org)

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]98

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]103

tf-seq2seq (google.github.io)

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

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

[How To Implement The Perceptron Algorithm From Scratch In Python]107

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]110

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]113

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)

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 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 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)

Math for Machine Learning (ucsc.edu)

Math for Machine Learning (UMIACS CMSC422)

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)

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)

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

[How To Understand Derivatives: The Product, Power & Chain Rules]150

Vector Calculus: Understanding the Gradient (betterexplained.com)

Differential Calculus (Stanford CS224n)

Calculus Overview (readthedocs.io)

作者：chen_h
微信号 & QQ：862251340
简书地址：http://www.jianshu.com/p/2be3...

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