Papers

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

• arxiv: http://arxiv.org/abs/1602.05419

Structured Sparse Regression via Greedy Hard-Thresholding

• arxiv: http://arxiv.org/abs/1602.06042

Evaluating Regularization and Optimization Algorithms for Logistic Regression on Spam Classification

• arxiv: http://rnowling.github.io/data/science/2016/09/04/comparing-lr-regularization-and-optimizers.html

Blogs

10 types of regressions. Which one to use?

• blog: http://www.datasciencecentral.com/profiles/blogs/10-types-of-regressions-which-one-to-use

Linear Regression for Machine Learning

http://machinelearningmastery.com/linear-regression-for-machine-learning/

Regression, Logistic Regression and Maximum Entropy

http://ataspinar.com/2016/03/28/regression-logistic-regression-and-maximum-entropy/

Principal Components Regression

• “Pt.1: The Standard Method”: http://www.win-vector.com/blog/2016/05/pcr_part1_xonly/

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow: LSTM regression using TensorFlow

• blog: http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html
• github: https://github.com/mouradmourafiq/tensorflow-lstm-regression

From simple Regression to Multiple Regression with Decision Trees

https://blog.cambridgecoding.com/2016/01/10/from-simple-regression-to-multiple-regression-with-decision-trees/

Linear Regression

http://efavdb.com/linear-regression/

What is Softmax regression and how is it related to Logistic regression?

-github: https://github.com/rasbt/python-machine-learning-book/blob/master/faq/softmax_regression.md

A Neat Trick to Increase Robustness of Regression Models

• blog: https://blog.clevertap.com/a-neat-trick-to-increase-robustness-of-regression-models/

Visualising Residuals

• blog: https://drsimonj.svbtle.com/visualising-residuals

Applications Of Isotonic Regression

http://jxieeducation.com/2016-09-18/Applications-of-Isotonic-Regression/

Bayesian Linear Regression

• github: https://github.com/liviu-/notebooks/blob/master/bayesian_linear_regression.ipynb

45 questions to test a Data Scientist on Regression (Skill test – Regression Solution)

https://www.analyticsvidhya.com/blog/2016/12/45-questions-to-test-a-data-scientist-on-regression-skill-test-regression-solution/

A comprehensive beginners guide for Linear, Ridge and Lasso Regression

https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/

Logistic regression

Guide to an in-depth understanding of logistic regression

• blog: http://www.dataschool.io/guide-to-logistic-regression/

Demystifying Logistic Regression

http://tech.magnetic.com/2016/04/demystifying-logistic-regression.html

The Sigmoid Function in Logistic Regression

http://karlrosaen.com/ml/notebooks/logistic-regression-why-sigmoid/

A Brief Primer on Linear Regression

• part I: https://blog.clevertap.com/a-brief-primer-on-linear-regression-part-i/
• part II: https://blog.clevertap.com/a-brief-primer-on-linear-regression-part-ii/
• part III: https://blog.clevertap.com/a-brief-primer-on-linear-regression-part-iii/

A Primer on Logistic Regression

• blog: https://blog.clevertap.com/a-primer-on-logistic-regression-part-i/

Logistic Regression in Tensorflow with SMOTE

• blog: http://aqibsaeed.github.io/2016-08-10-logistic-regression-tf/

Introduction to Logistic Regression

https://blog.bigml.com/2016/09/22/introduction-to-logistic-regression/

Logistic regression

• blog: http://eli.thegreenplace.net/2016/logistic-regression/
• github: https://github.com/eliben/deep-learning-samples/tree/master/logistic-regression

Ridge Regression

Lecture notes on ridge regression

• arxiv: https://arxiv.org/abs/1509.09169

Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging

• intro: UC Berkeley & Rensselaer Polytechnic Institute
• arxiv: https://arxiv.org/abs/1702.04837
• github: https://github.com/wangshusen/SketchedRidgeRegression

Support Vector Regressor (SVR)

A Tutorial on Support Vector Regression

• intro: Alex J. Smola. 2003
• paper: https://alex.smola.org/papers/2003/SmoSch03b.pdf