儘管機器學習的歷史可以追溯到1959年，但目前，這個領域正以前所未有的速度發展。最近，我一直在網上尋找關於機器學習和NLP各方面的好資源，為了幫助到和我有相同需求的人，我整理了一份迄今為止我發現的最好的教程內容列表。

        通過教程中的簡介內容講述一個概念。避免了包括書籍章節涵蓋範圍廣，以及研究論文在教學理念上做的不好的特點。

        我把這篇文章分成四個部分：機器學習、NLP、Python和數學。每個部分中都包含了一些主題文章，但是由於材料巨大，每個部分不可能包含所有可能的主題，我將每個主題限制在5到6個教程中。

機器學習

• 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)

啟用和損失函式

• 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)

感知器

• Perceptrons (neuralnetworksanddeeplearning.com)

• The Perception (natureofcode.com)

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

• From Perceptrons to Deep Networks (toptal.com)

迴歸

• 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)

梯度下降演算法

• 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)

生成式學習

• Generative Learning Algorithms (Stanford CS229)

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

支援向量機

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

• Support Vector Machines (Stanford CS229)

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

反向傳播

• 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)

深度學習

• 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)

優化和降維

• 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)

長短期記憶網路

• 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)

卷積神經網路

• 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)

遞迴神經網路

• 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)

強化學習

• 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)

生成對抗網路

• 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)

多工學習

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

自然語言處理

• 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)

深入學習和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)

詞向量

• 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)

例子

• 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和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)

數學

• Math for Machine Learning (ucsc.edu)

• Math for Machine Learning (UMIACS CMSC422)

線性代數

• 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)

概率

• 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)

微積分

• 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)

• Calculus Overview (readthedocs.io)

原文連結https://unsupervisedmethods.com/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78

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