The recent work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNN) in creating artistic fantastic imagery by separating and recombing the image content and style. This process of using CNN to migrate the semantic content of one image to different styles is referred to as Neural Style Transfer. Since then, Neural Style Transfer has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention from computer vision researchers and several methods are proposed to either improve or extend the original neural algorithm proposed by Gatys et al. However, there is no comprehensive survey presenting and summarizing recent Neural Style Transfer literature. This review aims to provide an overview of the current progress towards Neural Style Transfer, as well as discussing its various applications and open problems for future research. Neural Style Transfer: A Review

Apache Drill is an innovative distributed SQL engine designed to enable data exploration and analytics on non-relational datastores […] without having to create and manage schemas. […] It has a schema-free JSON document model similar to MongoDB and Elasticsearch; [a plethora of APIs, including] ANSI SQL, ODBC/JDBC, and HTTP[S] REST; [is] extremely user and developer friendly; [and, has a] pluggable architecture enables connectivity to multiple datastores.

“Once the often laborious task of data munging is complete, the next step in the data science process is to become intimately familiar with the data set by performing what’s called Exploratory Data Analysis (EDA). The way to gain this level of familiarity is to utilize the features of the statistical environment you’re using (R, Matlab, SAS, Python, etc.) that support this effort – numeric summaries, aggregations, distributions, densities, reviewing all the levels of factor variables, applying general statistical methods, exploratory plots, and expository plots.” Daniel Gutierrez ( November 5, 2014 )

The year is nearly over and it is the time for reflection and navel-gazing. Idon’t have incredibly profound things to say, but a lot of things happened in2018 and this is as good a time as any to go through it all…

A journalist asked me to look at a recent report, Analysis of degree classifications over time: Changes in graduate attainment.  The report was published by the UK government’s Office for Students (OfS) on 19 December 2018, along with a headline-grabbing press release:

Someone writes:

Chapter 2 of Data Visualization walks you through setting up an R Project, and takes advantage of R Studio’s support for RMarkdown templates. That is, once you’ve created your project in R Studio, can choose File > New File > R Markdown, like this:

By Nathalie Jeans.

Lying at the heart of modern data science and analysis, Jupyter Notebooks are an incredibly powerful tool at both ends of the project lifecycle. Whether you’re rapidly prototyping ideas, demonstrating your work, or producing fully fledged reports, notebooks can provide an efficient edge over IDEs or traditional desktop applications.

How to meet your New Years resolutions in 2019