Data Mining: Overviews


A Framework for Approaching Textual Data Science Tasks

@machinelearnbot

There's an awful lot of text data available today, and enormous amounts of it are being created on a daily basis, ranging from structured to semi-structured to fully unstructured. What can we do with it? Well, quite a bit, actually; it depends on what your objectives are, but there are 2 intricately related yet differentiated umbrellas of tasks which can be exploited in order to leverage the availability of all of this data. NLP is a major aspect of computational linguistics, and also falls within the realms of computer science and artificial intelligence. Text mining exists in a similar realm as NLP, in that it is concerned with identifying interesting, non-trivial patterns in textual data.


Top Stories, Jan 1-7: Docker for Data Science; Quantum Machine Learning: An Overview

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Docker for Data Science, by Sachin Abeywardana Top 10 Machine Learning Algorithms for Beginners, by Reena Shaw How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science? How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science? Top Stories, Dec 18-31: How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?; Computer Vision by Andrew Ng – 11 Lessons Learned - Jan 03, 2018. How to build a Successful Advanced Analytics Department - Jan 04, 2018. Top Stories, Dec 18-31: How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?; Computer Vision by Andrew Ng – 11 Lessons Learned - Jan 03, 2018.


Top Stories, Jan 1-7: Docker for Data Science; Quantum Machine Learning: An Overview

@machinelearnbot

Docker for Data Science Top 10 Machine Learning Algorithms for Beginners How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science? How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science? Top Stories, Dec 18-31: How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?; Computer Vision by Andrew Ng – 11 Lessons Learned - Jan 03, 2018. How to build a Successful Advanced Analytics Department - Jan 04, 2018. Top Stories, Dec 18-31: How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?; Computer Vision by Andrew Ng – 11 Lessons Learned - Jan 03, 2018.


Data Can Lie–Here's A Guide To Calling Out B.S.

@machinelearnbot

According to the University of Washington professors Carl T. Bergstrom and Jevin West, it's time someone did something about it. It's a free structured course of readings and case studies aimed at giving students (and anyone who might be interested) the tools to look critically at scientific claims driven by data and machine learning. Over the past six months, the two scientists created the syllabus and published it online in the hopes that the UW administration would take notice and turn it into a real class (it's currently winding its way through the approval process, and might be offered as soon as the spring). The two have been frustrated with the way statistical findings are treated in the media and in the classroom for years. West, a professor in the Information School and the director of UW's Data Lab, believes that thanks to the emergence of big data and the increasing availability of tools that help more people work with it, the amount of bullshit appears to have increased; with so much data out there, there is simply more potential for data scientists and designers to shape it to fit their own conclusions–or even intentionally mislead their audience.


Global Bigdata Conference

#artificialintelligence

In recent years, many tech giants (Google, Microsoft Azure, IBM) invested heavily in the general-use of Machine Learning and Deep Learning. In 2018, more SME businesses will learn how to use their solutions and full service platforms. They have managed to optimize Computer Vision and Natural Language Processing in such a way that it will most likely outperform any other (smaller) player in this field. With help of API's they will take over (market share up to 85%) the general-use machine learning industry in 2018. In 2017 there has been an exponential use of so called'click – drag and drop' tools.


Tech Trends 2018: An Overview

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Rather than exploring emerging technology domains in isolation, CIOs can orchestrate the convergence of powerful new capabilities to uncover business opportunities and achieve strategic and operational goals. "An orchestra full of stars," the renowned German conductor Kurt Masur said, "can be a disaster." It turns out the same can be true of an enterprise full of emerging technology capabilities developed and pursued independently. In our ninth annual Tech Trends report, "The Symphonic Enterprise," we look at how strategy, technology, and operations trends can work together in harmony across domains and boundaries in the coming year. Without a doubt, there is more than one next big thing in enterprise technology.


A Framework for Approaching Textual Data Science Tasks

@machinelearnbot

There's an awful lot of text data available today, and enormous amounts of it are being created on a daily basis, ranging from structured to semi-structured to fully unstructured. What can we do with it? Well, quite a bit, actually; it depends on what your objectives are, but there are 2 intricately related yet differentiated umbrellas of tasks which can be exploited in order to leverage the availability of all of this data. NLP is a major aspect of computational linguistics, and also falls within the realms of computer science and artificial intelligence. Text mining exists in a similar realm as NLP, in that it is concerned with identifying interesting, non-trivial patterns in textual data.


Monetizing the Internet of Things (IoT) @ThingsExpo #AI #IoT #M2M #BigData

@machinelearnbot

"Why incur the expense of generating and collecting all of this IoT data if you're not going to monetize it?" Organizations are racing to embrace the Internet of Things (IoT) as the pundits create "visions of sugar-plums dancing in their heads." McKinsey Global Institute released their study "The Internet of Things: Mapping the Value beyond the Hype" in June 2015 that highlighted the staggering financial value that IoT could create! The folks at Wikibon provided a perspective on the sources of "IoT monetization" in their recent research titled "Harvesting Value at the Edge" written by the always delightful and provocative Neil Raden. IoT, though a useful application of available technology, and well-defined at the hardware and network levels, the heart of IoT, that part that yields the real value, is edge analytics.


A Framework for Approaching Textual Data Science Tasks

@machinelearnbot

There's an awful lot of text data available today, and enormous amounts of it are being created on a daily basis, ranging from structured to semi-structured to fully unstructured. What can we do with it? Well, quite a bit, actually; it depends on what your objectives are, but there are 2 intricately related yet differentiated umbrellas of tasks which can be exploited in order to leverage the availability of all of this data. NLP is a major aspect of computational linguistics, and also falls within the realms of computer science and artificial intelligence. Text mining exists in a similar realm as NLP, in that it is concerned with identifying interesting, non-trivial patterns in textual data.


An Overview of ResNet and its Variants – Towards Data Science

#artificialintelligence

After the celebrated victory of AlexNet [1] at the LSVRC2012 classification contest, deep Residual Network [2] was arguably the most groundbreaking work in the computer vision/deep learning community in the last few years. ResNet makes it possible to train up to hundreds or even thousands of layers and still achieves compelling performance. Taking advantage of its powerful representational ability, the performance of many computer vision applications other than image classification have been boosted, such as object detection and face recognition. Since ResNet blew people's mind in 2015, many in the research community have dived into the secrets of its success, many refinements have been made in the architecture. This article is divided into two parts, in the first part I am going to give a little bit of background knowledge for those who are unfamiliar with ResNet, in the second I will review some of the papers I read recently regarding different variants and interpretations of the ResNet architecture.