TEXT ANALYSIS (NLP)
Full course on “traditional” Natural Language Processing, including sentimentanalysis, Naive Bayes models, n-grams, etc.Deep Learning for Natural Language Processing (Course), (Course materials) Thecurrent bleeding edge of Natural Language Processing. You should finish AndrewNg’s machine learning course first.
Fantastic breakdown of recurrent neural networks, which are special applications ofdeep learning especially successful in natural language processing.
Step-by-step tutorial of implementing a recurrent neural network in Python’s keraspackage.15
Introduction to collaborative filters using Python. Does a very nice job of explainingthe intuition behind the algorithm.
Discussion of the theory and math behind collaborative filters by Andrew Ng. Moremath-heavy, and it’ll be easier to follow if you have some background with LinearAlgebra.
Reference tutorial that implements a music recommender system in Python.
The same tutorial as the previous one, except in R.16
TIME SERIES ANALYSIS
Lecture slides, homework, and R Code for the Time Series course at Oregon StateUniversity.
Very practical step-by-step introduction to using R for time series analysis. Includes code and outputs for each step.
Tutorial on performing time series visualization, analysis, and forecasting with Python.
Introduction to ARIMA models in Python. Includes all code.
Course notes from the statistical forecasting course taught at the Fuqua School ofBusiness at Duke University.17
Relatively little-known hidden gem, but one of our favorite resources for learningabout neural networks. Explanations are clear and intuitive.
Comprehensive online book that covers a wide range of topics in deep learning.
Tech talks by Yann LeCun, one of the “Godfathers” of modern deep learning.
Course taught by Geoff Hinton, one of the other “Godfathers” of modern deeplearning.
Neural networks and deep learning taught from the perspective of a computerscientist. Heavy on code and light on math.Stanford 231n: Convolutional Neural Networks (Course Notes), (Lecture Videos)Rigorous course on convolutional neural networks for computer vision.
Step-by-step tutorial for building deep neural networks with Google Tensorflow.
Building a neural network from scratch in R.18
Part of Andrew Ng’s excellent machine learning course. We recommend startinghere.
Short (66-page) textbook on anomaly detection. Excellent introduction with intuitiveexplanations.
Shortacademic overview of anomaly detection techniques. Useful to get a lay of the land.
Tutorial using the sklearn library to perform anomaly detection in Python.
Tutorial using the AnomalyDetection package in R.