May 17, 2017   |   Yinyin Liu

Partnership on AI

At Nervana and now at Intel, our data scientists work directly with domain experts to solve real-world problems using AI across a broad set of industries including agriculture, healthcare, automotive, energy, and finance. We spend our time building connections and applying deep learning to address each use case, and we are finding that the problem…

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Jan 06, 2017   |   Yinyin Liu

Building Skip-Thought Vectors for Document Understanding

The idea of converting natural language processing (NLP) into a problem of vector space mathematics using deep learning models has been around since 2013. A word vector, from word2vec [1], uses a string of numbers to represent a word’s meaning as it relates to other words, or its context, through training. From a word vector,…

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#Model Zoo #NLP

Dec 29, 2016   |   Jennifer Myers

neon v1.8.0 released!

Highlights from this release include:  * Skip Thought Vectors example * Dilated convolution support * Nesterov Accelerated Gradient option to SGD optimizer * MultiMetric class to allow wrapping Metric classes * Support for serializing and deserializing encoder-decoder models * Allow specifying the number of time steps to evaluate during beam search * A new community-contributed Docker image…

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#Release Notes

Dec 08, 2016   |   Anthony Ndirango

End-to-end speech recognition with neon

By: Anthony Ndirango and Tyler Lee Speech is an intrinsically temporal signal. The information-bearing elements present in speech evolve over a multitude of timescales. The fine changes in air pressure at rates of hundreds to thousands of hertz convey information about the speakers, their location, and help us separate them from a noisy world. Slower changes in…

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#Model Zoo #Speech Recognition

Nov 28, 2016   |   Jessica Rosenthal

#IntelAI Day

On November 17th, 2016, Intel hosted its first ever “AI Day” at Bespoke in San Francisco. #IntelAI soared to the top of Twitter’s trending hashtags as nearly 500 people piled in to watch Intel’s Brian Krzanich (CEO), Diane Bryant (EVP & GM Data Center Group), Doug Fisher (SVP & GM Software), and Doug Davis (SV IOTG) deliver…

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Nov 22, 2016   |   Jennifer Myers

neon v1.7.0 released!

Highlights from this release include:  Update Data Loader to aeon for flexible, multi-threaded data loading and transformations. More information can be found in the docs, but in brief, aeon: provides an easy interface to adapt existing models to your own, custom, datasets supports images, video and audio and is easy to extend with your own providers for custom…

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#Release Notes

Nov 17, 2016   |   Jason Knight

Preview Release: Intel® Nervana™ Graph

The field of deep learning is moving at a rapid pace.  Practitioners need tools that are flexible enough to keep up. Theano popularized the notion of computational graphs as a powerful abstraction, and more recently, TensorFlow iterated on that concept. Together, they demonstrate some first steps in unlocking the potential of deep learning, but we…

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Oct 12, 2016   |   Sathish Nagappan

Accelerating Neural Networks with Binary Arithmetic

At Nervana we are deeply interested in algorithmic and hardware improvements for speeding up neural networks. One particularly exciting area of research is in low precision arithmetic. In this blog post, we highlight one particular class of low precision networks named binarized neural networks (BNNs), the fundamental concepts underlying this class, and introduce a Neon…

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Sep 22, 2016   |   Jennifer Myers

neon v1.6.0 released!

Highlights from this release include:  Faster RCNN model Sequence to Sequence container and char_rae recurrent autoencoder model Reshape Layer that reshapes the input[#221] Pip requirements in requirements.txt updated to latest versions [#289] Remove deprecated data loaders and update docs Use NEON_DATA_CACHE_DIR envvar as archive dir to store DataLoader ingested data Eliminate type conversion for FP16…

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#Release Notes

Sep 22, 2016   |   Carlos Morales

Security at Nervana Part 2: Securing Data

In our previous Security post, we discussed the Root of Trust, and how it is used to create a secure, trusted environment in which to execute deep learning applications. In this post, we explore the challenges involved in securing data, and how we can build on the aforementioned hardened software environment to meet those challenges.…

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#Intel DL Cloud & Systems