- 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.
Collinearity, Cross-validation, Feature Selection, Python, Random
- 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.
Deep Learning, Logistic Regression, Neural Networks, Noise, Random, Sampling, word2vec
- 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?
Parallelism, Programming, Random, Randomization
- 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.
AI, Machine Learning, Random
- 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.
Apache Spark, GitHub, Random, RDD
- 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.
Complexity, Probability, Random, Randomization
- 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.
Academics, ICML, NIPS, Random, Randomization
- 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.
Correlation, Random
- 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.
Correlation, Overfitting, Quandl, Random
- 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.
Data Mining Books, Dilbert, R, Random
- 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).
Cartoon, Deep Learning, Dilbert, Free ebook, Neural Networks, Random
