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.
Book, Data Science, Ian Goodfellow, Machine Learning, Mathematics, Robert Tibshirani, Vladimir Vapnik
- 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, Isabelle Guyon, Support Vector Machines, SVM, Vladimir Vapnik
- 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.
About Gregory Piatetsky, Alan Turing, Bayes Theorem, Data Mining, DJ Patil, History, Vladimir Vapnik
- 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.
Berlin, Deep Learning, Machine Learning, Support Vector Machines, SVM, Vladimir Vapnik, Yandex, Zachary Lipton
- 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.
Andrew Ng, Deep Learning, Facebook, Interview, NYU, Support Vector Machines, Vladimir Vapnik, Yann LeCun
