Machine Learning

• Start Here with Machine Learning (machinelearningmastery.com)
• Machine Learning is Fun! (medium.com/@ageitgey)
• Rules of Machine Learning: Best Practices for ML Engineering (martin.zinkevich.org)
• 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)
• The Machine Learning Primer (sas.com)
• Machine Learning Tutorial for Beginners (kaggle.com/kanncaa1)

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

• A Guide to Deep Learning by YN² (yerevann.com)
• Deep Learning Papers Reading Roadmap (github.com/floodsung)
• 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)
• Deep Learning — The Straight Dope (gluon.mxnet.io)
• 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)

• Adversarial Machine Learning (aaai18adversarial.github.io)
• 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

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

NLP

• Natural Language Processing is Fun! (medium.com/@ageitgey)
• 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

• Machine Learning Crash Course (google.com)
• Awesome Machine Learning (github.com/josephmisiti)
• 7 Steps to Mastering Machine Learning With Python (kdnuggets.com)
• An example machine learning notebook (nbviewer.jupyter.org)
• Machine Learning with Python (tutorialspoint.com)

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)
• ML from Scatch (github.com/eriklindernoren)
• Python Machine Learning (2nd Ed.) Code Repository (github.com/rasbt)

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)