PostgreSQL, MonetDB, and Too-Big-for-Memory Data in R — Part I
In a blog from three months ago, I wrote on “kind of” big data in R. The “kind of” was meant as a caveat that data size in R is limited by RAM. I...
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How to Execute R and Python in SQL Server with Machine Learning Services
Introduction Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQLServer eliminates the need for data movem...
Statisticians, like artists, have the bad habit of falling in love with their models
This is a quote by George E Box. Share if you like it. In short, all models are approximations. All models are wrong, but some are useful. George E Box (18 October 1919 ...
Generalized Machine Learning – Kerneml – Simple ML to train Complex ML
I recently created a ‘particle optimizer’ and published a pip python package called kernelml. The motivation for making this algorithm was to give analysts and data s...
Decision-driven before data-driven : A roadmap for data-driven organizations
THE THREE GOOD DECISIONS.. Some thirty years back, when I started my career as a management-trainee with an Aerospace company; the 18 months long training program include...
American Community Survey data in R
I met up with an old grad school friend a few weeks back. He’s an accomplished professor of demography, basically the statistical study of populations, particularl...
27 Great Resources About Logistic Regression
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, c...
Deep Learning: AlphaGo Zero Explained In One Picture
Recently Google DeepMind announced AlphaGo Zero — an extraordinary achievement that has shown how it is possible to train an agent to a superhuman level in the high...
Inside the black box 2
Following the original observations of Neural Networks in action; I decided a follow up was needed. In the original blog, ; the smallest neural net (NN) that learnt the...
Intuition behind Bias-Variance trade-off, Lasso and Ridge Regression
Linear regression uses Ordinary Least square method to find the best coefficient estimates. One of the assumptions of Linear regression is that the variables are not corr...