A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation

A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation

R Interface to Access the ‘SEMrush’ API (SEMrushR)Implements methods for querying SEO (Search Engine Optimization) and SEM (Search Engine Marketing) data from ‘SEMrush’ using its API («a href=”http …

Teaching R is our mission at Business Science University because R is the most efficient language for exploring data, performing business analysis, and applying data science to business to extract ROI for an organization. R has an amazing ecosystem of tools that seemlessly work together, which has been termed the “tidyverse”. The “tidyverse” ecosystem can take business analysis to a new level. Here’s the key: 99.9% of business analysts don’t know R (and its awesome power). With our learning system, you can learn R quickly, setting yourself on a career tragectory in data science.

A new release 0.4.13 of RProtoBuf got onto CRAN a few hours ago. RProtoBuf provides R with bindings for the Google Protocol Buffers (“ProtoBuf”) data encoding and serialization library used and released by Google, and deployed fairly widely in numerous projects as a language and operating-system agnostic protocol.

coalesce is a classic useful SQL operator that picks the first non-NULL value in a sequence of values.

It’s been about a year and a half since I joined Automattic as a remote data scientist. This is the longest I’ve been in one position since finishing my PhD in 2012. This is also the first time I’ve worked full-time with a fully-distributed team. In this post, I briefly discuss some of the top pluses and minuses of remote work, based on my experience so far.

A Monte Carlo Valuation Framework for Variable Annuities (vamc)Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of variable annuities, which reflect realistic features of common an …

Multiview Learning Multi-view learning is an emerging direction in machine learning which considers learning with multiple views to improve the generalization performance. Multi-view learning is also known as data fusion or data integration from multiple feature sets. …

And here is Le Monde mathematical puzzle  last competition problem