Tag: visualization

Feed: Featured Blog Posts - Data Science Central. Author: Andreas Blumauer. Tips Don't try to put the cart before the horse: realize that efficient data preparation (and thus interoperable standards) and data quality, especially in the enterprise environment, are a basic requirement for all applications of artificial intelligence. The development of competences and experts in the field of artificial intelligence must take place at least parallel to the process of every technological decision, but not at the end of the implementation of an AI strategy. Outsourcing must not be part of this strategy. ‘Not to boil the ocean’, in other ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Divya Singh. Machine Learning is the word of the mouth for everyone involved in the analytics world. Gone are those days of the traditional manual approach of taking key business decisions. Machine Learning is the future and is here to stay. However, the term Machine Learning is not a new one. It was there since the advent of computers but has grown tremendously in the last decade due to the massive amounts of data that’s getting generated, and the enormous computational power that modern-day system possesses. Machine Learning is the art ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Marco Tavora. Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are some examples of AI-based applications that already affect us every day. Deep learning in areas such as speech recognition, autonomous driving, machine translation, and visual object recognition has been systematically improving the state of the art for a while now.However, the reasons that make deep neural networks (DNN) so powerful are only heuristically understood, i.e. we know only from experience that we can achieve ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Ariful Islam. Unlike supervised learning, unsupervised learning not working with labeled data, it is not showing the machine the correct answer. Instead, it is using different algorithms to let the machine create connections by studying and observing the data. Learn much of this through study and observation. Learning and improving by trial and error is the key to unsupervised learning. However, the Knowledge Discovery process is the field of data mining is concerned with the development of methods, techniques and algorithm which can make sense of the available data. It is useful in finding trends, patterns, correlations ... Read More

Feed: R-bloggers. Author: Posts on R Lover ! a programmer. So, a programmer, a frequentist, and a bayesian walk into a bar. No this postisn’t really on the path to some politically incorrect stereotypical humor. Juttrying to make it fun and catch your attention. As the title implies this postis really about applying the differing viewpoints and methodologies inherent inthose approaches to statistics. To be honest I’m not even going to spend a lottime on the details of methods, I want to focus on the conclusions each of thesepeople would draw after analyzing the same data using their favored methods.This ... Read More

Feed: R-bloggers. Author: R Views. Andrie de Vries is the author of “R for Dummies” and a Solutions Engineer at RStudio Phillip (Armand) Bester is a medical scientist, researcher, and lecturer at the Division of Virology, University of the Free State, and National Health Laboratory Service (NHLS), Bloemfontein, South Africa In this post we complete our series on analysing the HIV pandemic in Africa. Previously we covered the bigger picture of HIV infection in Africa, and a pipeline for drug resistance testing of samples in the lab. Then, in part 3 we saw that sometimes the same patient’s genotype must ... Read More

Feed: R-bloggers. Author: . As I was preparing some graphics for a presentation recently, I started digging into some of the different color palette options. My motivation was entirely about creating graphics that weren’t too visually overwhelming, which I found the default “rainbow” palette to be. But as the creators of the viridis R package point out, we also need to think about how people with colorblindness might struggle with understanding graphics. If you create figures in R, I highly recommend checking it out at the link above! To leave a comment for the author, please follow the link and ... Read More

Feed: R-bloggers. Author: chris2016. Principal component analysis (PCA) is very useful for doing some basic quality control (e.g. looking for batch effects) and assessment of how the data is distributed (e.g. finding outliers). A straightforward way is to make your own wrapper function for prcomp and ggplot2, another way is to use the one that comes with M3C (https://bioconductor.org/packages/devel/bioc/html/M3C.html) or another package. M3C is a consensus clustering tool that makes some major modifications to the Monti et al. (2003) algorithm so that it behaves better, it also provides functions for data visualisation. Let’s have a go on an example cancer ... Read More

Feed: R-bloggers. Author: Colin Fay. How to build a CLI for R, with npm. Background This blog post was triggered by a discussion on Twitter with MartinSkarzynski,who was looking for a way to build a CLI that launches an RScript.Here’s a way to do this using npm. Please note that this blog post won’t teach you how to build the commandline tool, it will quickly go over the way to create a system-widecommand line interface, using npm. If you want to learn more about building the utility, see thisfantastic series of blogpostsby Mark Sellor. Now, the idea is to have ... Read More

Feed: SAS Blogs. Author: Emily Gao. My colleague Wayne Thompson has written a series of blogs about machine learning best practices. In this series' fifth post, Autotune models to avoid local minimum breakdowns, Wayne discusses two autotune methods, grid search and Bayesian optimization. According to another colleague’s paper, Automated Hyperparameter Tuning for Effective Machine Learning, Bayesian optimization is currently popular for hyperparameter optimization. Recently, I read a post on Github which demonstrated the Bayesian optimization procedure through a great demo using Python, and I wondered if I could build the same with the SAS matrix language, SAS/IML. In this article, ... Read More