Saturday, 21 May 2016 07:26

Learning About Deep Learning

The concept is certainly compelling. Having a machine capable of reacting to real-world visual, auditory or other type of data and then responding, in an intelligent way, has been the stuff of science fiction until very recently. We are now on the verge of this new reality with little general understanding of what it is that artificial intelligence, convolutional neural networks, and deep learning can (and can’t) do, nor what it takes to make them work. At the simplest level, much of the current efforts around deep learning involve very rapid recognition and classification of objects—whether visual, audible, or some other form of digital data. Using cameras, microphones and other types of sensors, data is input into a system that contains a multi-level set of filters that provide increasingly detailed levels of differentiation. Think of it like the animal or plant classification charts from your grammar school days: Kingdom, Phylum, Class, Order, Family, Genus, Species.

Published in Data Science Tools

You may be thinking that this title makes no sense at all. ML, AI, ANN and Deep learning have made it into the everyday lexicon and here I am, proclaiming that ML is dead. Well, here is what I mean…

The open sourcing of entire ML frameworks marks the end of a phase of rapid development of tools, and thus marks the death of ML as we have known it so far. The next phase will be marked with ubiquitous application of these tools into software applications. And that is how ML will live forever, because it will seamlessly and inextricably integrate into our lives.

Published in Data Science Tools

Integrates the theory and applications of statistics using R. A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models.

Published in General News

On Tuesday, analytics company SAS announced Viya, its new analytics and visualization architecture. A SAS spokeswoman said Viya would become the "foundation for all future SAS products."

The goal of Viya is to make analytics more accessible to all users and to better support cloud-native apps and data stored in the cloud.

The announcement came at the 2016 SAS Global Forum, the company's annual conference for SAS users and executives, which took place in Las Vegas. Viya will be available as part of an early adopter program in May, but it will reach general availability sometime in Q3 of 2016.

Published in General News

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SDAT is an abbreviation for Scientific Data Analysis Team. It consists of groups who are specialists in various fields of data sciences including Statistical Analytics, Business Analytics, Big Data Analytics and Health Analytics. 

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