收藏 | 机器学习、NLP、Python和Math最好的150余个教程

尽管机器学习的历史可以追溯到1959年,但目前,这个领域正以前所未有的速度发展。最近,我一直在网上寻找关于机器学习和NLP各方面的好资源,为了帮助到和我有相同需求的人,我整理了一份迄今为止我发现的最好的教程内容列表。

通过教程中的简介内容讲述一个概念。避免了包括书籍章节涵盖范围广,以及研究论文在教学理念上做的不好的特点。

我把这篇文章分成四个部分:机器学习、NLP、Python和数学。

每个部分中都包含了一些主题文章,但是由于材料巨大,每个部分不可能包含所有可能的主题,我将每个主题限制在5到6个教程中。(由于微信不能插入外链,请点击“阅读原文”查看原文)

机器学习

  • 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)

激活和损失函数

  • 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)

回归

  • 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)

生成式学习

  • Generative Learning Algorithms (Stanford CS229)
  • A practical explanation of a Naive Bayes classifier (monkeylearn.com)

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)

反向传播

  • 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)

深度学习

  • 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)

优化和降维

  • 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)

长短期记忆网络

  • 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)

卷积神经网络

  • 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)

递归神经网络

  • 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)

强化学习

  • 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)

生成对抗网络

  • 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)

多任务学习

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

自然语言处理

  • 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)

深入学习和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)

词向量

  • 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)

数学

  • Math for Machine Learning (ucsc.edu)
  • Math for Machine Learning (UMIACS CMSC422)

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)

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)
  • Calculus Overview (readthedocs.io)

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

原文发布于微信公众号 - CDA数据分析师(cdacdacda)

原文发表时间:2018-01-22

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