by Anusua Trivedi, Microsoft Data Scientist Background and Approach This blog series is based on my upcoming talk on re-usability of Deep Learning Models at the Hadoop+Strata World Conference in Singapore. This blog series will be in several parts – where I describe my experiences and go deep into the reasons behind my choices. Deep learning is an emerging field of research, which has its application across multiple domains. I try to show how transfer learning and fine tuning strategy leads to re-usability of the same Convolution Neural Network model in different disjoint domains. Application of this model acros
NoSQL databases have been around for several years now and have become a choice of data storage for managing semi-structured and unstructured data. These databases offer lot of advantages in terms of linear scalability and better performance for both data writes and reads. InfoQ spoke with four panelists to get different perspectives on the current state of NoSQL databases.
Apache Spark 2.0.0 is the first release on the 2.x line. The major updates are API usability, SQL 2003 support, performance improvements, structured streaming, R UDF support, as well as operational improvements.
I know, you are tired of hearing about Deep Learning. Who isn’t by now? But programming has been stuck in a rut for a very long time and it's time we do something about it. Lots of silly little programming wars continue to be fought that decide nothing. Functions vs objects; this language vs that language; this public cloud vs that public cloud vs this private cloud vs that ‘fill in the blank’; REST vs unrest; this byte level encoding vs some different one; this framework vs that framework; this methodology vs that methodology; bare metal vs containers vs VMs vs unikernels; monoliths vs microservices vs nanoservices; eventually consistent vs transactional; mutable vs immutable; DevOps vs NoOps vs SysOps; scale-up vs scale-out; centralized vs decentralized; single threaded vs massively parallel; sync vs async. And so on ad infinitum.
People often get stuck when they are asked to improve the performance of existing predictive models. What usually they do is try different algorithms and check their results. But often they end up not improving the model. Here are some of the steps you can take to boost your existing models. 1. Add more dataRead More
This app ranks the popularity of dozens of programming languages. You can filter them by listing only those most relevant to particular sectors, such as “Web” or “embedded programming.” Rankings are created by weighting and combining 12 metrics from 10 sources. We offer preset weightings—the default is our IEEE Spectrum ranking—but there are presets for those interested in what's trending or most looked for by employers. Don't like the defaults? Take complete control and create your own ranking by adjusting each metric's weighting yourself. To compare with previous year's data, add a comparison and then choose “edit ranking,” which will give you the option to compare with data from 2014 or 2015.
Where do customers abandon the shopping process? Is it the same in every geography? Audience of One.... Who are your fans versus haters in the marketplace? How do customers feel about your products? How engaged are customers with your brand versus your competitors’ brands across social media and web channels? Fortune 500 companies are making large investments…
he Adapt Intent Parser is an open source software library for converting natural language into machine readable data structures. Adapt is lightweight and streamlined and is designed to run on devices with limited computing resources. Adapt takes in natural language and outputs a data structure that includes the intent, a match probability, a tagged list of entities. The software was developed at Mycroft AI by a team led by Sean Fitzgerald, formerly one of the developers of both Siri and Amazon Echo.
I come from the relational world like probably everyone who's been doing software for the last 5 years or so. In 2012 NoSQL databases were already a thing, and I had an opportunity to work with Google's Datastore, so I googled the documentation and started from the beginning.
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