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.
With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely …
Image super-resolution through deep learning. This project uses deep learning to upscale 16x16 images by a 4x factor. The resulting 64x64 images display sharp features that are plausible based on the dataset that was used to train the neural net.
Asynchronous end-to-end calls starting from the view layer to the backend is important in a microservices architecture because there is no guarantee that the containers which receive the calls will handle the response.
Earlier this week, we announced the latest release of the TF-Slim library for TensorFlow, a lightweight package for defining, training and evaluating models, as well as checkpoints and model definitions for several competitive networks in the field of image classification.
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.
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