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Workshop / Chrome Experiments

Workshop / Chrome Experiments | EEDSP | Scoop.it
Welcome to the Workshop! Here you'll find tools and projects created by the team behind Chrome Experiments.
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EEDSP
Digital Signal Processing, Data Analytics, Big Data, HPC, Deep Learning, GPGPU
Curated by Shiwon Cho
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Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition | EEDSP | Scoop.it

PLOS Computational Biology is an open-access

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Top 10 Programming Languages in 2014

Top 10 Programming Languages in 2014 | EEDSP | Scoop.it

This report was prepared using anonymous metadata from more than 300,000 users of Codeanywhere. Keep in mind that all users from this report are using Codeanywhere, which is a web-based IDE currently intended mostly for web developers.

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A Deep Learning Tutorial: From Perceptrons to Deep Networks

A Deep Learning Tutorial: From Perceptrons to Deep Networks | EEDSP | Scoop.it

Are you joining the growing group of developers who want to know more about Deep Learning? This introductory tutorial covers it all.

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The third great wave

The third great wave | EEDSP | Scoop.it
MOST PEOPLE ARE discomfited by radical change, and often for good reason. Both the first Industrial Revolution, starting in the late 18th century, and the second...
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New Study Reveals Most Influential Languages | IFLScience

New Study Reveals Most Influential Languages | IFLScience | EEDSP | Scoop.it
What makes a language important on a global scale? Is it the oldest? The one spoken by the most people? What about the one that has the greatest ability to reach other people by being translated? A multidisciplinary research team has examined the languages of the world and categorized them on how widely certain forms of media are translated into other languages. César Hidalgo of MIT led the research, and the paper was published in the Proceedings of the National Academy of Sciences.
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Yandex technologies. MatrixNet

Yandex technologies. MatrixNet | EEDSP | Scoop.it
The job of a search engine is, first and foremost, to provide answers to user’s queries. In response to each query, a search engine returns links to web pages it finds in its index – a database of web pages known to this particular search engine. Thus, an answer to the user’s query comes in the form of search results – a list of hyperlinks to web pages, whose content matches this query.
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Google releases Android Studio 1.0, the first stable version of its IDE

Google releases Android Studio 1.0, the first stable version of its IDE | EEDSP | Scoop.it
After two years of development, Google today released Android Studio 1.0, the first stable version of its Integrated Development Environment (IDE) aimed at Android developers. You can download it r...
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H2O - Fast Scalable Machine Learning For Smart Applications

H2O makes Hadoop do Math! H2O scales statistics, machine learning and math over Big Data. H2O keeps familiar interfaces like R, Excel & JSON so that big data enthusiasts and experts can explore, munge, model and score data sets using a range of simple to advanced algorithms.
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ThisPlusThat: A Search Engine That Lets You 'Add' Words as Vectors

ThisPlusThat: A Search Engine That Lets You 'Add' Words as Vectors | EEDSP | Scoop.it

Natural language isn't that great for searching. When you type a search query into Google, you miss out on a wide spectrum of human concepts and human emotions. Queried words have to be present in the web page, and then those pages are ranked according to the number of inbound and outbound links. That's great for filtering out the cruft on the internet -- and there's a lot of that out there. What it doesn't do is understand the relationships between words and understand the similarities or dissimilarities.

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Facebook's Memcached Multiget Hole: More machines != More Capacity  - High Scalability -

When you are on the bleeding edge of scale like Facebook is, you run into some interesting problems. As of 2008 Facebook had over 800 memcached servers supplying over 28 terabytes of cache. With those staggering numbers it's a fair bet to think they've seen their share of Dr. House worthy memcached problems.

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How League of Legends Scaled Chat to 70 million Players - It takes Lots of minions. - High Scalability -

How League of Legends Scaled Chat to 70 million Players - It takes Lots of minions. - High Scalability - | EEDSP | Scoop.it
How would you build a chat service that needed to handle 7.5 million concurrent players, 27 ...
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Amazon Kinesis - Real-Time Processing of Streaming Big Data

Amazon Kinesis - Real-Time Processing of Streaming Big Data | EEDSP | Scoop.it

Amazon Kinesis is a managed service designed to handle real-time streaming of big data. It can accept any amount of data, from any number of sources, scaling up and down as needed.

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Announcing Azure Stream Analytics for real-time event processing | Microsoft Azure Blog

Announcing Azure Stream Analytics for real-time event processing | Microsoft Azure Blog | EEDSP | Scoop.it

At TechEd Europe 2014, Microsoft announced the preview of Azure Stream Analytics. Stream Analytics is a real-time event processing engine that helps uncover insights from devices, sensors, infrastructure, applications, and data. With out-of-the-box integration to Event Hubs, the combined solution can both ingest millions of events as well as do analytics to better understand patterns, power a dashboard, detect anomalies, and kick off an action while data is being streamed in real-time.

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Modern Microprocessors - A 90 Minute Guide!

Modern Microprocessors - A 90 Minute Guide! | EEDSP | Scoop.it

WARNING: This article is meant to be informal and fun!

Okay, so you're a CS graduate and you did a hardware/assembly course as part of your degree, but perhaps that was a few years ago now and you haven't really kept up with the details of processor designs since then.

In particular, you might not be aware of some key topics that developed rapidly in recent times...

pipelining (superscalar, OoO, VLIW, branch prediction, predication)multi-core & simultaneous multithreading (SMT, hyper-threading)SIMD vector instructions (MMX/SSE/AVX, AltiVec)caches and the memory hierarchy

Fear not! This article will get you up to speed fast. In no time you'll be discussing the finer points of in-order vs out-of-order, hyper-threading, multi-core and cache organization like a pro.

But be prepared – this article is brief and to-the-point. It pulls no punches and the pace is pretty fierce (really). Let's get into it...

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Blend it like a Bayesian!: Things to try after useR! - Part 1: Deep Learning with H2O

Blend it like a Bayesian!: Things to try after useR! - Part 1: Deep Learning with H2O | EEDSP | Scoop.it

Deep Learning with H2O + R

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THE FUTURE OF MOBILE: 2014 [SLIDE DECK]

THE FUTURE OF MOBILE: 2014 [SLIDE DECK] | EEDSP | Scoop.it
The mobile industry is shifting. Mobile revenue is moving into software and services.
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Why Neural Networks Look Set to Thrash the Best Human Go Players for the First Time | MIT Technology Review

Why Neural Networks Look Set to Thrash the Best Human Go Players for the First Time | MIT Technology Review | EEDSP | Scoop.it
One of the last bastions of human mastery over computers is about to fall to the relentless onslaught of machine learning algorithms.
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Top 14 Big Data Books of 2014

Top 14 Big Data Books of 2014 | EEDSP | Scoop.it
2014 has been a huge year in big data- and big data publishing. Viktor Mayer-Schoenberger and Kenneth Cukier re-published and added an extra chapter to their bestselling Big Data; Nate Silver graced t
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Zeppelin

Zeppelin | EEDSP | Scoop.it

A web-based notebook that enables interactive data analytics. 
You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.

Watch the video

 
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NoSQL Data Modeling Techniques

NoSQL Data Modeling Techniques | EEDSP | Scoop.it
NoSQL databases are often compared by various non-functional criteria, such as scalability, performance, and consistency. This aspect of NoSQL is well-studied both in practice and theory because sp...
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Implementing a Distributed Deep Learning Network over Spark – Data Science Central

Implementing a Distributed Deep Learning Network over Spark – Data Science Central | EEDSP | Scoop.it

Deep learning is becoming an important AI paradigm for pattern recognition, image/video processing and fraud detection applications in finance. The computational complexity of a deep learning network dictates need for a distributed realization. Our intention is to parallelize the training phase of the network and consequently reduce training time. We have built the first prototype of our distributed deep learning network over Spark, which has emerged as a de-facto standard for realizing machine learning at scale.

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The Unicode Blog: New Emoji Candidates for Unicode 8.0

The Unicode Blog: New Emoji Candidates for Unicode 8.0 | EEDSP | Scoop.it

The Unicode Consortium has accepted 37 new emoji characters as candidates for Unicode 8.0, scheduled for mid-2015. These are candidates—not yet finalized—so some may not appear in the release.

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How to become a data scientist in 8 easy steps: the infographic

How to become a data scientist in 8 easy steps: the infographic | EEDSP | Scoop.it
This post was written by the team behind DataCamp, the online interactive learning platform for data science.   After being dubbed “sexiest job of the 21st Century” by Harvard Business Review, data scientists have stirred the interest of the general public. Many people are intrigued by this job, namely because the name has an interesting […]
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The Onion Architecture : part 1 : Jeffrey Palermo (.com)

The Onion Architecture : part 1 : Jeffrey Palermo (.com) | EEDSP | Scoop.it
This is part 1. part 2 . part 3 . part 4 . My feed (rss) . I've spoken several times about a specific type of architecture I call "Onion Architecture". I've found that it leads to more maintainable applications since it emphasizes separation of concerns
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