Organizing Analytics like the Human Brain
Artificial Intelligence (A.I.) is a wide umbrella of emerging technologies which have the potential to completely transform business and society. Key components include: ...
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Some Irresistible Integrals, Computed Using Statistical Concepts
Updated on Dec 12, 2018. An error was fixed when g(x) is not equal to x, and a new section “Generalization” was added. A link to a large collections of intrig...
Anatomy of an AI System
This article was written by Kate Crawford & Vladan Joler. Below is an extract, featuring the first three sections of this long article (21 sections total.) Link to t...
Deep Learning Research Review: Natural Language Processing
This article was written by Adit Deshpande on his own blog. This is the 3rd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I...
Why Excel Users Should Learn Python
Latest update: November 16, 2018 Microsoft Excel has been around for over 30 years now, and chances are it’s not going to change in the foreseeable future. In fact,...
50+ Examples of How Blockchains are Taking Over the World
This article was written by Matteo Gianpietro Zago. When talking about blockchains, we commonly think of its applications in the future. “Blockchain will solve this, bl...
Automated Machine Learning Hyperparameter Tuning in Python
This article was written by William Koehrsen A complete walk through using Bayesian optimization for automated hyperparameter tuning in Python Tuning machine learning hyp...
Scale-Invariant Clustering and Regression
The impact of a change of scale, for instance using years instead of days as the unit of measurement for one variable in a clustering problem, can be dramatic. It can res...
Deep Dive into Polynomial Regression and Overfitting
In this article, we show that the issue with polynomial regression is not over-fitting, but numerical precision. Even if done right, numerical precision still remains an ...
Deep Dive into Polynomial Regression and Overfitting
In this article, we show that the issue with polynomial regression is not over-fitting, but numerical precision. Even if done right, numerical precision still remains an ...