In this tutorial, you will get to know the two packages that are popular to work with geospatial data: geopandas and Shapely. Then you will apply these two packages to read in the geospatial data using Python and plotting the trace of Hurricane Florence from August 30th to September 18th.
When working with data it is helpful to build a correlation matrix to describe data and the associations between variables. In this article, you learn how to use visualizations for correlation matrices in R.
As my Masters is coming to an end, I wanted to work on an interesting NLP project where I can use all the techniques(not exactly) I have learned at USF. With the help of my professors and discussions with the batch mates, I decided to build a question-answering model from scratch. I am using the Stanford Question Answering Dataset (SQuAD). The problem is pretty famous with all the big companies trying to jump up at the leaderboard and using advanced techniques like attention based RNN models to get the best accuracy. All the GitHub repositories that I found related to SQuAD by other people have also used RNNs.
I use generalized additive models (GAMs) in my research work. I use them a lot! Simon Wood’s mgcv package is an excellent set of software for specifying, fitting, and visualizing GAMs for very large data sets. Despite recently dabbling with brms, mgcv is still my go-to GAM package. The only down-side to mgcv is that it is not very tidy-aware and the ggplot-verse may as well not exist as far as it is concerned. This in itself is no bad thing, though as someone who uses mgcv a lot but also prefers to do my plotting with ggplot2, this lack of awareness was starting to hurt. So, I started working on something to help bridge the gap between these two separate worlds that I inhabit. The fruit of that labour is gratia, and development has progressed to the stage where I am ready to talk a bit more about it. gratia is an R package for working with GAMs fitted with gam(), bam() or gamm() from mgcv or gamm4() from the gamm4 package, although functionality for handling the latter is not yet implement. gratia provides functions to replace the base-graphics-based plot.gam() and gam.check() that mgcv provides with ggplot2-based versions. Recent changes have also resulted in gratia being much more tidyverse aware and it now (mostly) returns outputs as tibbles. In this post I wanted to give a flavour of what is currently possible with gratia and outline what still needs to be implemented.
The RStudio 1.2 Preview Release makes it even easier to create RESTful Web APIs in R using the plumber package. plumber is a package that converts your existing R code to a web API using a handful of special one-line comments.
So you’ve deployed your machine learning model to the cloud and all of your apps and services are able to fetch predictions from it, nice! You can leave that model alone to do its thing forever… maybe not. Most machine learning models are modeling something about this world, and this world is constantly changing. Either change with it, or be left behind!
up is the Ultimate Plumber, a tool for writing Linux pipes in a terminal-based UI interactively, with instant live preview of command results. The main goal of the Ultimate Plumber is to help interactively and incrementally explore textual data in Linux, by making it easier to quickly build complex pipelines, thanks to a fast feedback loop. This is achieved by boosting any typical Linux text-processing utils such as grep, sort, cut, paste, awk, wc, perl, etc., etc., by providing a quick, interactive, scrollable preview of their results.