作者：chen_h
微信號 & QQ：862251340
微信公眾號：coderpai
我的博客：請點擊這里

我把這篇文章分為四個部分：機器學習，NLP，Python 和 數學。我在每一部分都會包含一些關鍵主題，但是網上資料太廣泛了，所以我不可能包括每一個可能的主題。

如果你發現好的教程，請告訴我。在這篇文章中，我把每個主題的教程數量都是控制在五到六個，這些精選出來的教程都是非常重要的。每一個鏈接都會鏈接到別的鏈接，從而導致很多新的教程。

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]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)

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]29

• [Logistic Regression Tutorial for Machine Learning]30

• 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]35

• 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]45

• Backpropagation Through Time and Vanishing Gradients (wildml.com)

• [A Gentle Introduction to Backpropagation Through Time]47

• 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]57

• 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]62

• 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]68

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

Reinforcement Learning

• [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)

Generative Adversarial Networks (GANs)

• 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)

Multi-task Learning

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

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]89

• 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]98

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]103

• 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]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 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/@erikhallst…)

• 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]150

• Vector Calculus: Understanding the Gradient (betterexplained.com)

• Differential Calculus (Stanford CS224n)

• Calculus Overview (readthedocs.io)

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總結

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