作者：chen_h
微信号 & QQ：862251340
微信公众号：coderpai
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
- 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)
Activation and Loss Functions
- 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)
Perceptron
- Perceptrons (neuralnetworksanddeeplearning.com)
- The Perception (natureofcode.com)
- Single-layer Neural Networks (Perceptrons) (dcu.ie)
- From Perceptrons to Deep Networks (toptal.com)
Regression
- 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)
Gradient Descent
- 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)
Generative Learning
- Generative Learning Algorithms (Stanford CS229)
- A practical explanation of a Naive Bayes classifier (monkeylearn.com)
Support Vector Machines
- An introduction to Support Vector Machines (SVM) (monkeylearn.com)
- Support Vector Machines (Stanford CS229)
- Linear classification: Support Vector Machine, Softmax (Stanford 231n)
Backpropagation
- 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)
Deep Learning
- 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)
Optimization and Dimensionality Reduction
- 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)
Long Short Term Memory (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)
Convolutional Neural Networks (CNNs)
- 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)
Recurrent Neural Nets (RNNs)
- 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)
Reinforcement Learning
- 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)
Generative Adversarial Networks (GANs)
- 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)
Multi-task Learning
- An Overview of Multi-Task Learning in Deep Neural Networks(sebastianruder.com)
NLP
- 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 and 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)
Word Vectors
- 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)
Examples
- 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 and 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)
Math
- Math for Machine Learning (ucsc.edu)
- Math for Machine Learning (UMIACS CMSC422)
Linear algebra
- 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)
Probability
- 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)
Calculus
- 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)
来源：Medium