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 LearningMachine 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 FunctionsSigmoid 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)
- BiasRole of Bias in Neural Networks (stackoverflow.com)
- Bias Nodes in Neural Networks (makeyourownneuralnetwork.blogspot.com)
- What is bias in artificial neural network? (quora.com)
- PerceptronPerceptrons (neuralnetworksanddeeplearning.com)
- The Perception (natureofcode.com)
- Single-layer Neural Networks (Perceptrons) (dcu.ie)
- From Perceptrons to Deep Networks (toptal.com)
- RegressionIntroduction 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 DescentLearning 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 LearningGenerative Learning Algorithms (Stanford CS229)
- A practical explanation of a Naive Bayes classifier (monkeylearn.com)
- Support Vector MachinesAn introduction to Support Vector Machines (SVM) (monkeylearn.com)
- Support Vector Machines (Stanford CS229)
- Linear classification: Support Vector Machine, Softmax (Stanford 231n)
- BackpropagationYes 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 LearningDeep 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 ReductionSeven 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 LearningSimple 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 LearningAn Overview of Multi-Task Learning in Deep Neural Networks(sebastianruder.com)
- NLPA 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 NLPDeep 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 VectorsBag 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-DecoderAttention 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)
- Python7 Steps to Mastering Machine Learning With Python (kdnuggets.com)
- An example machine learning notebook (nbviewer.jupyter.org)
- ExamplesHow 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)
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