# Random (11)

**Feature selection by random search in Python**- Aug 6, 2019.

Feature selection is one of the most important tasks in machine learning. Learn how to use a simple random search in Python to get good results in less time.**A Gentle Introduction to Noise Contrastive Estimation**- Jul 25, 2019.

Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.**Are Vectorized Random Number Generators Actually Useful?**- Aug 28, 2018.

I reported that you can multiply the speed of common (fast) random number generators such as PCG and xorshift128+ by a factor of three or four by vectorizing them using SIMD instructions. Is this actually useful in practice?**Chaos is needed to keep us smart with Machine Learning**- Jul 20, 2018.

This post analyses why the chaotic nature of our lives can be used to improve machine learning algorithms.**Pitfalls in pseudo-random number sampling at scale with Apache Spark**- Jun 27, 2017.

Large scale simulation of random number generation is possible with today’s high speed & scalable distributed computing frameworks. Let’s understand how it can be achieved using Apache Spark.**The Surprising Complexity of Randomness**- Jun 15, 2017.

The reason we have pseudorandom numbers is because generating true random numbers using a computer is difficult. Computers, by design, are excellent at taking a set of instructions and carrying them out in the exact same way, every single time.**Embrace the Random: A Case for Randomizing Acceptance of Borderline Papers**- May 16, 2016.

A case for using randomization in the selection of borderline academic papers, a particular use case which has parallels with many other possible scenarios.**Random vs Pseudo-random – How to Tell the Difference**- Oct 26, 2015.

Statistical know-how is an integral part of Data Science. Explore randomness vs. pseudo-randomness in this explanatory post with examples.**Surprising Random Correlations**- May 14, 2015.

An interesting demo showing how easy it is to find surprising correlations in real data. Is German unemployment rate related to Apple Stock? Is 10-year Treasury rate related to price of Red Winter Wheat? You will be surprised.**Year in Review: Top KDnuggets tweets in September**- Dec 30, 2014.

One pattern is random, other is machine-generated. Can you guess which?; 14 Awesome (and Free) #DataScience Books; Dilbert 20 funniest cartoons on #BigData, data mining, privacy; Watch: Statistical, Machine learning with R, great 15 hour online course.**Top KDnuggets tweets, Sep 19-21: Dilbert funniest cartoons on #BigData, data mining; Guess which pattern is random**- Sep 22, 2014.

Guess which pattern is random, which machine-generated? Dilbert 20 funniest cartoons on #BigData, data mining, privacy; Data Scientist Cartoon; Neural Networks and Deep Learning, free online book (draft).