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Machine Learning is the Solution to the Big Data Problem Caused by the IoT - IT Peer Network

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In more complex situations, a machine learning algorithm may build a complex model based on big data from transactions across the entire population of users to improve the accuracy of fraud detection. Developers can extract maximum performance from Intel hardware by using the library of math kernels and optimized algorithms from Intel called Intel Data Analytics Acceleration Library (Intel DAAL) and Intel Math Kernel Library (Intel MKL). By using the Intel-optimized frameworks supported by Intel MKL, I've seen customers get performance on deep learning network topologies including convolutional neural networks (CNN) and recurrent neural networks (RNN) that is an order of magnitude greater than running these frameworks un-optimized on commodity CPUs. For qualified organizations, we can provide test and development platforms based on the Intel Xeon Phi processor, software, tools and training, as well as reference architectures and blueprints to accelerate the deployment of enterprise-grade solutions.


Intel Launches 'Knights Landing' Phi Family for HPC, Machine Learning

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From ISC 2016 in Frankfurt, Germany, this week, Intel Corp. launched the second-generation Xeon Phi product family, formerly code-named Knights Landing, aimed at HPC and machine learning workloads. "We're not just a specialized programming model," said Intel's General Manager, HPC Compute and Networking, Barry Davis in a hand-on technical demo held at ISC. "Knights Landing" also puts integrated on-package memory in a processor, which benefits memory bandwidth and overall application performance. The Pascal P100 GPU for NVLink-optimized servers offers 5.3 teraflops of double-precision floating point performance, and the PCIe version supports 4.7 teraflops of double-precision.


Scary and fascinating: The future of big data

ZDNet

ZDNet recently spoke to Bernard Marr, author of the book, Big Data: Using smart big data analytics and metrics to make better decisions and improve performance, to see what's next for the technology. People thought that if Google is sharing all this hospital information then Google will know too much about me. What fascinates me is combining big data with machine learning and especially natural language processing, where computers do the analysis by themselves to find things like new disease patterns, to find them in the data. A lot of these big companies -- companies like Google, Amazon, and so on -- the people who run those companies will become richer and richer and most of the others will not be able to participate in wealth generation.


Senior Engineer in Recommender Systems/siliconarmada.com

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Real-time signal: Traditional recommender systems for Big Data are based on batch computation. What do we do for those will a small user base and little traffic? Our R&D team of close to 300 engineers is building the next generation digital advertising technologies that allow us to manage billions of ad impressions every day. A few figures: â 15 datacenters (8 with computing capacity 7 dedicated to network connectivity) Â across US, EU, APAC â More than 15K servers, running a mix of Linux and Windows â Two of the largest Hadoop clusters in Europe each with close to 40PB of storage and 30.000 cores â 30B HTTP requests and close to 3B unique banners displayed per day â Close to 1M HTTP requests per second handled during peak times â 40Gbps of bandwidth, half of it through peering exchanges We are located in the heart of Paris.


First big data and machine learning system for engineering simulation

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SeaScape is claimed to allow organisations to innovate faster than the ever by bringing together the advanced computer science of elastic computing, big data and machine learning and the physics-based world of engineering simulation. By leveraging such big data technologies as elastic compute and map reduce, SeaScape is said to provide an infrastructure to address these issues in the context of almost any engineering design objective. The first product on the SeaScape infrastructure, SeaHawk, dramatically transforms electronic product design through improvements in simulation coverage, turnaround times and analysis flexibility. "Die size and development time reduction are targets that electronic design engineers have pursued with marginal success given the limitations of today's in-design solutions," said John Lee, general manager, ANSYS.


Distributed Deep Learning with Caffe Using a MapR Cluster

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Google, Baidu, and Microsoft have the resources to build dedicated deep learning clusters that give the deep learning algorithms a level of processing power that both accelerates training time as well as increases their model's accuracy. Yahoo, however, has taken a slightly different approach, by moving away from a dedicated deep learning cluster and combining Caffe with Spark. The ML Big Data team's CaffeOnSpark software has allowed them to run the entire process of building and deploying a deep learning model onto a single cluster. The MapR Converged Data Platform is the ideal platform for this project, giving you all the power of distributed Caffe on a cluster with enterprise-grade robustness, enabling you to take advantage of the MapR high performance file system.


5 Ways Machine Learning Is Reshaping Our World

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More importantly, however, Google and its competitors are moving towards keying their search algorithms to understand natural speech as well, in anticipation of more and more voice search. But new machine learning algorithms are making more accurate, real-time translations possible. You might also be interested in my new big data case study collection, which you can download for free from here: Big Data Case Study Collection: 7 Amazing Companies That Really Get Big Data. My current book is Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance' and my new books (available to pre-order now) are Key Business Analytics: The 60 Business Analysis Tools Every Manager Needs To Know and Big Data in Practice.


Textio's Learning Machine Offers Opportunities to Improve HR Writing Xconomy

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Textio, a Seattle machine learning and natural language processing startup focused on improving job listings and recruiting e-mails for companies including Starbucks, Microsoft, and Twitter, is releasing a new feature that can identify words and phrases that are not bad, per se, but could be better. Textio highlights words and phrases that attract or repel candidates for specific jobs in specific geographies, and even from specific demographic groups. But making several small tweaks--Textio finds between two and 10 opportunities for improvement in a typical document--can add up to a job listing that performs significantly better. Machine learning, natural language processing, and big data feed its predictive engine--essentially a set of tailored algorithms--which does its magic in a fraction of a second, thanks to commoditized computing power available for rent in public clouds.


Making Deep Learning accessible on Openstack

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This week at the Openstack Developers Summit we are excited to showcase how Canonical with IBM, Mesosphere, Skymind and Data Fellas are working together to make the opportunities of deep learning easier for everyone to access. This is important since we quickly encounter significant constraints when building and deploying big software and big data projects. That is why we have been working with partners such as IBM Power Systems, Mesosphere, Skymind and Data Fellas. The first thing that we created is a model with Juju, Canonical's Application Modelling Framework, that automates the process for building a complete, classic, deep learning stack.


2025: Artificial Intelligence and the recruiting revolution

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We then have to look at the future to find some comfort and try to understand if the big wave of innovation we see today (artificial intelligence, virtual and augmented reality, wearables, big data, Internet of Things etc…) will impact positively on recruiting. On top of these benefits, it should also add a bit of quality: not everybody who says they want a project manager or an IT lead or a financial analyst means exactly the same thing, an artificial intelligence will navigate through business titles giving them a real meaning thanks to data clustering techniques; at the same time, it will be able to score and sort resumes without any individual bias. People data analytics: we have big data and we adopt analytics widely, it makes sense this trend will impact recruiting as well. Real time monitoring of performance and behavior: if a candidate can influence his digital identity, the most important information for any company will anyway come from his / her past performance and behavior.