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X-47B demonstrator makes first catapult launch

X-47B demonstrator makes first catapult launch | EEDSP | Scoop.it
The X-47B Unmanned Combat Air System (UCAS) demonstrator has taken yet another step on the path towards unmanned aircraft operating amongst piloted aircraft...
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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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Grumpy: Go running Python!

Grumpy: Go running Python! | EEDSP | Scoop.it

Grumpy is an experimental Python runtime for Go. It translates Python code into Go programs, and those transpiled programs run seamlessly within the Go runtime. We needed to support a large existing Python codebase, so it was important to have a high degree of compatibility with CPython (quirks and all). The goal is for Grumpy to be a drop-in replacement runtime for any pure-Python project.

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GANs will change the world

GANs will change the world | EEDSP | Scoop.it
It’s New Year’s 2017, so time to make predictions. Portfolio diversification has never been me, so I’ll make just one. Generative Adversarial Networks — GANs for short — will be the next big thing in…
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A Guide to Deep Learning by YerevaNN

Deep learning is a fast-changing field at the intersection of computer science and mathematics. It is a relatively new branch of a wider field called machine learning.
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Deep Learning Cheat Sheet

Deep Learning Cheat Sheet | EEDSP | Scoop.it
Deep Learning can be overwhelming when new to the subject. Here are some cheats and tips to get you through it. In this article we will go over common concepts found in Deep Learning to help get…
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OpenNMT · NMT

OpenNMT · NMT | EEDSP | Scoop.it

OpenNMT is a full-featured, open-source (MIT) neural machine translation system utilizing the Torch mathematical toolkit.

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Opinions crossing the table: Khronos for world peace - StreamComputing

Opinions crossing the table: Khronos for world peace - StreamComputing | EEDSP | Scoop.it
At SC16 there was a discussion between programming language standards for heterogeneous hardware, organised by Khronos. See here for the setup of the session
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MXNet - Deep Learning Framework of Choice at AWS - All Things Distributed

MXNet - Deep Learning Framework of Choice at AWS - All Things Distributed | EEDSP | Scoop.it
At Amazon, machine learning has been key to many of our business processes, from recommendations to fraud detection, from inventory levels to book classification to abusive review detection. And there are many more application areas where we use machine learning extensively: search, autonomous drones, robotics in fulfillment centers, text and speech recognitions, etc.
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NASA Confirms: 'StarTrek' EM-Drive Propulsion System "Appears to Work --To Mars in 70 Days"

NASA Confirms: 'StarTrek' EM-Drive Propulsion System "Appears to Work --To Mars in 70 Days" | EEDSP | Scoop.it
After months of heated debate and leaked documents, NASA's long-awaited EM Drive paper has finally been peer-reviewed and published. And it shows that the 'impossible' propulsion system appears to work. Tests carried out by both NASA and independent researcher
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GPU Acceleration in Databricks

GPU Acceleration in Databricks | EEDSP | Scoop.it
With Spark deployments tuned for GPUs, plus pre-installed libraries and examples, Databricks offers a simple way to leverage GPUs to power image processing, text analysis, and other Machine Learning tasks.
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Differentiable neural computers | DeepMind

Differentiable neural computers | DeepMind | EEDSP | Scoop.it
In a study in Nature, we introduce a form of memory-augmented neural network called a differentiable neural computer, and show that it can learn to use its memory to answer questions about complex, structured data, including artificially generated stories, family trees, and even a map of the London Underground. We also show that it can solve a block puzzle game using reinforcement learning.
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CUDA 8 Features Revealed

CUDA 8 Features Revealed | EEDSP | Scoop.it

CUDA Toolkit 8 Now Available.


CUDA 8 adds support for the Pascal GPU architecture, new Unified Memory capabilities, the new nvGraph GPU-Accelerated Graph Analytics library, new profiling

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Generative Adversarial Networks – Hot Topic in Machine Learning

Generative Adversarial Networks – Hot Topic in Machine Learning | EEDSP | Scoop.it

What is Generative Adversarial Networks (GAN) ? A very illustrative explanation of GAN is presented here with simple examples like predicting next frame in video sequence or predicting next word while typing in google search.

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Machine Learning Crash Course: Part 2 · ML@B

Machine Learning Crash Course: Part 2 · ML@B | EEDSP | Scoop.it

In this post we’ll talk about one of the most fundamental machine learning algorithms: the perceptron algorithm. This algorithm forms the basis for many modern day ML algorithms, most notably neural networks. In addition, we’ll discuss the perceptron algorithm’s cousin, logistic regression. And then we’ll conclude with an introduction to SVMs, or support vector machines, which are perhaps one of the most flexible algorithms used today.

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MNIST Generative Adversarial Model in Keras

MNIST Generative Adversarial Model in Keras | EEDSP | Scoop.it

This post discusses and demonstrates the implementation of a generative adversarial network in Keras, using the MNIST dataset.

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Neural Network Architectures

Neural Network Architectures | EEDSP | Scoop.it

Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture.

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Open sourcing the Embedding Projector: a tool for visualizing high dimensional data

Open sourcing the Embedding Projector: a tool for visualizing high dimensional data | EEDSP | Scoop.it

Recent advances in Machine Learning (ML) have shown impressive results, with applications ranging from image recognition, language translation, medical diagnosis and more. With the widespread adoption of ML systems, it is increasingly important for research scientists to be able to explore how the data is being interpreted by the models. However, one of the main challenges in exploring this data is that it often has hundreds or even thousands of dimensions, requiring special tools to investigate the space.

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Semantic Scholar

Semantic Scholar | EEDSP | Scoop.it
An academic search engine that utilizes artificial intelligence methods to provide highly relevant results and novel tools to filter them with ease.
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Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System

Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System | EEDSP | Scoop.it

In “Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation”, we address this challenge by extending our previous GNMT system, allowing for a single system to translate between multiple languages. Our proposed architecture requires no change in the base GNMT system, but instead uses an additional “token” at the beginning of the input sentence to specify the required target language to translate to. In addition to improving translation quality, our method also enables “Zero-Shot Translation” — translation between language pairs never seen explicitly by the system.

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Tetris: Multi-Resource Packing for Cluster Schedulers

Tetris: Multi-Resource Packing for Cluster Schedulers | EEDSP | Scoop.it
Tetris is a cluster scheduler that packs, i.e., matches multi-resource task requirements with resource availabilities of machines.
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Introducing vtop — A Terminal Activity Monitor in Node.js - Parallax

Command-line tools like "top" make it difficult to see CPU usage across multi-proces
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TensorFlow Wide & Deep Learning Tutorial

TensorFlow Wide & Deep Learning Tutorial | EEDSP | Scoop.it
logistic regression model to predict the probability that the individual has an annual income of over 50,000 dollars using the Census Income Dataset.
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Announcing YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research

Announcing YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research | EEDSP | Scoop.it
Google announce the release of YouTube-8M, a dataset of 8 million YouTube video URLs (representing over 500,000 hours of video), along with video-level labels from a diverse set of 4800 Knowledge Graph entities. This represents a significant increase in scale and diversity compared to existing video datasets.
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