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8 real-world big data deployments

8 real-world big data deployments | EEDSP | Scoop.it
The amount of data in the world doubles every 18 months. Here's a look at eight real-world big data deployments in a variety of industries.
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EEDSP
Digital Signal Processing, Data Analytics, Big Data, HPC, Deep Learning, GPGPU, Distributed and Parallel Computing
Curated by Shiwon Cho
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LUIS: Language Understanding Intelligent Service

Language Understanding Intelligent Service (LUIS) offers a fast and effective way of adding language understanding to applications.
With LUIS, you can use pre-existing, world-class, pre-built models from Bing and Cortana whenever they suit your purposes -- and when you need specialized models,
LUIS guides you through the process of quickly building them.
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NVIDIA TensorRT

NVIDIA TensorRT | EEDSP | Scoop.it

TensorRT 2 Released


TensorRT 2 is now available as a free download to the members of the NVIDIA Developer Program. Deliver up to 45x faster inference under 7 ms real-time latency with INT8 precision Integrate novel user defined layers as plugins using Custom Layer API Deploy sequence based models for image captioning, language translation and other applications using LSTM and GRU Recurrent Neural Networks (RNN) layers

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Google’s Tensor2Tensor makes it easier to conduct deep learning experiments

Google’s Tensor2Tensor makes it easier to conduct deep learning experiments | EEDSP | Scoop.it
Google's brain team is open sourcing Tensor2Tensor, a new deep learning library designed to help researchers replicate results from recent papers in the field..
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Google releases new TensorFlow Object Detection API

Google releases new TensorFlow Object Detection API | EEDSP | Scoop.it
Google is releasing a new TensorFlow object detection API to make it easier for developers and researchers to identify objects within images. Google is trying..
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DeepMind Research – Kinetics | DeepMind

Kinetics is a large-scale, high-quality dataset of YouTube video URLs which include a diverse range of human focused actions. Our aim in releasing the Kinetics dataset is to help the machine learning community to advance models for video understanding.
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Deep Dress

Submit a photo and Deep Dress AI will use deep learning techniques to visually look at dresses find you something to rent.

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A neural approach to relational reasoning | DeepMind

A neural approach to relational reasoning | DeepMind | EEDSP | Scoop.it
A key challenge in developing artificial intelligence systems with the flexibility and efficiency of human cognition is giving them an ability for relational reasoning - drawing logical conclusions about how physical objects, sentences, or even abstract ideas are related to one another. In two new papers, we explore the ability for deep neural networks to perform complicated relational reasoning with unstructured data. In A Simple Neural Network Module for Relational Reasoning we describe a Relation Network and show that it can perform at superhuman levels on a challenging task. While in Visual Interaction Networks we describe a general purpose model that can predict the future state of a physical object based purely on visual observations.
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Microsoft’s high-performance, open source, deep learning toolkit is now generally available - Microsoft Cognitive Toolkit

Microsoft’s high-performance, open source, deep learning toolkit is now generally available - Microsoft Cognitive Toolkit | EEDSP | Scoop.it
Microsoft Cognitive Toolkit version 2.0 is now in full release with general availability. Cognitive Toolkit enables enterprise-ready, production-grade AI by allowing users to create, train, and evaluate their own neural networks that can then scale efficiently across multiple GPUs and multiple machines on massive data sets. The 2.0 version of the toolkit, previously known as CNTK, started in bet
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techiexpert's curator insight, June 2, 5:07 AM

Microsoft Cognitive Toolkit version 2.0 is now in full release with general availability.

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The AI Revolution Is Eating Software: NVIDIA Is Powering It | NVIDIA Blog

The AI Revolution Is Eating Software: NVIDIA Is Powering It | NVIDIA Blog | EEDSP | Scoop.it
At this year’s GTC Conference, NVIDIA showed how it is delivering AI for every computing platform, every deep learning framework. Read more.
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Facebook Publishes New Neural Machine Translation Algorithm

Facebook Publishes New Neural Machine Translation Algorithm | EEDSP | Scoop.it
Facebook’s Artificial Intelligence Research team published research results using a new approach for neural machine translation (NMT). Their algorithm scores higher than any other system on three established machine translation tasks.
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Machine learning evolution (infographic)

Machine learning evolution (infographic) | EEDSP | Scoop.it
What will it take for AI to become mainstream in business? The convergence of different research approaches—and lots of human intelligence.
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Which machine learning algorithm should I use?

Which machine learning algorithm should I use? | EEDSP | Scoop.it
This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest.
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Inside Volta: The World’s Most Advanced Data Center GPU | Parallel Forall

Inside Volta: The World’s Most Advanced Data Center GPU | Parallel Forall | EEDSP | Scoop.it
NVIDIA Tesla V100 is the most advanced data center GPU ever built to accelerate AI, HPC, and Graphics. This post details the Volta GPU architecture.
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Deep learning on Apache Spark and Apache Hadoop with Deeplearning4j - Cloudera Engineering Blog

Deep learning on Apache Spark and Apache Hadoop with Deeplearning4j - Cloudera Engineering Blog | EEDSP | Scoop.it
In late 2016, Ben Lorica of O’Reilly Media declared that “2017 will be the year the data science and big data community engage with AI technologies.” Deep learning on GPUs has pervaded universities and research organizations prior to 2017, but distributed deep learning on CPUs is now beginning to gain widespread adoption in a diverse set of companies and domains. While GPUs provide top-of-the-line performance in numerical computing, CPUs are also becoming more efficient and much of today’s existing hardware already has CPU computing power available in bulk. Read More
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ParlAI

ParlAI | EEDSP | Scoop.it
ParlAI (pronounced “par-lay”) is a framework for dialog AI research, implemented in Python.
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Big Data with Golang Instead of MapReduce

This is one of those software engineering ideas that I would normally warn you about. So many people use MapReduce that it seems foolhardy to use something else. But in this case, it turned out well. The project was a success, and we were able to accomplish our goals more quickly and with fewer resources than it would have taken with a MapReduce cluster. Background I work on 3d Warehouse for Trimble SketchUp (formerly Google). One of our focuses over the last year has been analytics - both for our customers and for our own internal use. Most business intelligence providers are expensive - anywhere from 100-500K per year. Even with that price point, it's still cheaper than engineering time, so that was originally the path we took.
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GOAI: Open GPU-Accelerated Data Analytics | Parallel Forall

GOAI: Open GPU-Accelerated Data Analytics | Parallel Forall | EEDSP | Scoop.it
Blog post demonstrates how GPU Data Frames from the GPU Open Analytics Initiative (GOAI) allow GPU-accelerated data analytics using standard data formats.
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Azure/mmlspark

Azure/mmlspark | EEDSP | Scoop.it
mmlspark - Microsoft Machine Learning for Apache Spark
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Learn Deep with Google’s AutoML

Learn Deep with Google’s AutoML | EEDSP | Scoop.it

Learn Deep with Google’s AutoML


In the Google’s I/O Conference 2017, Sundar Pichai (CEO, Google) talked about the new project called – AutoML. This new project focuses on deep learning - a technique that involves passing the data through layers of neural networks.

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End-to-end Computation on the GPU with the GPU Data Frame (GDF)

End-to-end Computation on the GPU with the GPU Data Frame (GDF) | EEDSP | Scoop.it
One of the things we are most excited about as a newly open source company is the potential to help kickstart a larger ecosystem of GPU computing. This is why we are particularly excited about our work with Continuum Analytics and H2O.ai to found the GPU Open Analytics Initiative
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Compute Library for Deep Neural Networks (clDNN) | 01.org

Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL inference on Intel® HD Graphics Driver and  Intel® Iris® graphics (also referred to as Intel® Processor Graphics). clDNN includes highly optimized building blocks to implement convolutional neural networks (CNN) with C and C++ interfaces.
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High Performance Analytics with GPU Acceleration - Analytics Industry News

High Performance Analytics with GPU Acceleration - Analytics Industry News | EEDSP | Scoop.it
Historically we have heard a lot about CPU and in-memory computing. What exactly is GPU acceleration and why should analytics pros care about it?
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Why Programmers Should Automate More

Why Programmers Should Automate More | EEDSP | Scoop.it
Last week, I created a small script to aid my workflow which left me wondering why I didn’t make it earlier. It adds an option in Finder’s context menu to start a static web server in any folder which makes it easier to preview static websites. Technically, it’s equivalent to firing up the terminal, cding to the directory, and using one of the many options to create a static server. The convenience of doing in one step which took three is minor, but automating the process is also advantageous in secondary ways. The crucial benefit is removing the tedious chunk—even if a small one—of my work. I can take pride in how my real work won’t include time to get a static server running.

Everyone’s daily work is filled with similar tedious chunks insomuch that it’s hard to distinguish the real work. Time spent in checking reports buried down a web of links; in collating data from several sources; in syncing files via email. In programming? In creating a build with five manual steps; in setting up workflow that requires several applications to be up and running. Time spent in these trivial tasks eats into the real work; worse, creates an impression that real work is getting done.
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CUDA 9 Features Revealed: Volta, Cooperative Groups and More | Parallel Forall

CUDA 9 Features Revealed: Volta, Cooperative Groups and More | Parallel Forall | EEDSP | Scoop.it
The CUDA 9 release includes support for Volta GPUs, Cooperative Groups programming model extensions, faster libraries, and improved developer tools.
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