- 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning - Apr 17, 2018.
It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
- Data Mining History: The Invention of Support Vector Machines - Jul 4, 2016.
The story starts in Paris in 1989, when I benchmarked neural networks against kernel methods, but the real invention of SVMs happened when Bernhard decided to implement Vladimir Vapnik algorithm.
- History of Data Mining - Jun 22, 2016.
Data mining is a subfield of computer science which blends many techniques from statistics, data science, database theory and machine learning. Here are the major milestones and “firsts” in the history of data mining plus how it’s evolved and blended with data science and big data.
- Does Deep Learning Come from the Devil? - Oct 9, 2015.
Deep learning has revolutionized computer vision and natural language processing. Yet the mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.
- Top KDnuggets tweets, Nov 24-30: How Word Meanings Change; Data wrangling with R: Things I wish I’d been told - Dec 1, 2014.
Linguistic Mapping Reveals How Word Meanings Sometimes Change Overnight; Practical #DataScience Cookbook, helps data practitioner; Facebook #AI team hires Vladimir Vapnik, father of #SVM; Starting data analysis/wrangling with R: Things I wish I'd been told.
- Top KDnuggets tweets, Nov 26-28: Facebook AI team hires Vladimir Vapnik, father of SVM - Nov 29, 2014.
Facebook's #AI team hires Vladimir Vapnik, father of popular #SVM algorithm; Starting data analysis/wrangling with R: Things I wish I'd been told; How to deal with missing values - advice from @Knime #DataMining; Understanding The Various Sources of #BigData - Infographic.
- KDnuggets Exclusive: Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab - Feb 20, 2014.
We discuss what enabled Deep Learning to achieve remarkable successes recently, his argument with Vapnik about (deep) neural nets vs kernel (support vector) machines, and what kind of AI can we expect from Facebook.