Over 200 of the Best Machine Learning, NLP, and Python Tutorials — 2018 Edition

(nvidia.

com)Deep Learning — The Straight Dope (gluon.

mxnet.

io)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)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 LearningAn Overview of Multi-Task Learning in Deep Neural Networks(sebastianruder.

com)NLPNatural 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 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)PythonMachine 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)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)ML from Scatch (github.

com/eriklindernoren)Python Machine Learning (2nd Ed.

) Code Repository (github.

com/rasbt)Scipy and numpyScipy 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-learnPyCon 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)TensorflowTensorflow 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)PyTorchPyTorch 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)MathMath for Machine Learning (ucsc.

edu)Math for Machine Learning (UMIACS CMSC422)Linear algebraAn 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)ProbabilityUnderstanding 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)CalculusHow To Understand Derivatives: The Quotient Rule, Exponents, and Logarithms (betterexplained.

com)How To Understand Derivatives: The Product, Power & Chain Rules(betterexplained.

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com)Differential Calculus (Stanford CS224n)Calculus Overview (readthedocs.

io)For more on machine learning, visit InfiniaML.

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