May 11, 2016   |   Evren Tumer

Industry Focus: Serving the Automotive Industry with the Nervana Platform

Introduction Accurate and expeditious semantic segmentation of an image is critical for applications like autonomous driving. The objects in the scene need to be localized and placed into categories to identify items like the road, the sidewalk, street signs, pedestrians, and other vehicles. In a recent attempt at solving this problem, a group from University…

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#Intel DL Cloud & Systems

Apr 30, 2016   |   Jennifer Myers

neon v1.4.0 released!

Highlights from this release include:  * VGG16 based Fast R-CNN model using winograd kernels * new, backward compatible, generic data loader * C3D video loader model trained on UCF101 dataset * Deep Dream example * make conv layer printout more informative [#222] * fix some examples to use new arg override capability * improve performance…

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#Release Notes

Apr 27, 2016   |   JD Co-Reyes

Blog Post (Part III): Deep Reinforcement Learning with OpenAI Gym

This is part 3 of a blog series on deep reinforcement learning. See “Part 1: Demystifying Deep Reinforcement Learning” for an introduction to the topic and “Part 2: Deep Reinforcement Learning with Neon” for the original implementation in Simple-DQN. In this blog post we will extend a Simple-DQN to work with OpenAI Gym, a new…

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#neon #Reinforcement Learning

Mar 04, 2016   |   Scott Gray

"Not so fast, FFT": Winograd

Deep learning thrives on speed. Faster training enables the construction of larger and more complex networks to tackle new domains such as speech or decision making. Recently, small convolutional filter sizes have become an important component in convolutional neural networks such as Google’s AlphaGo network or Microsoft’s deep residual networks. While most convolutions are computed…

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Mar 03, 2016   |   Arjun Bansal

Customer Focus: Blue River and the Future of Agricultural Robotics

At Nervana, we wanted to share with you some of our work in the agriculture industry to help optimize crop yields, and overall operations. Improving the success of these crops will help solve the food shortage crisis as the human population grows exponentially. This is something that we can all be grateful for. Blue River…

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Feb 09, 2016   |   Hunter Lang

Using neon for Scene Recognition: Mini-Places2

Introduction Much of the latest research in computer vision has focused on deep learning techniques. It has been applied to object recognition, where the goal is to predict what type of object is pictured in an image, and object localization, where the goal is to predict an object’s location in an image. Scene recognition is…

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#Model Zoo #Scene Recognition

Feb 02, 2016   |   Arjun Bansal

neon v1.2 release: Kepler & AWS support are back, Deep ResNets, and more

We are excited to share neon’s v1.2 release with the community, which has several major features (Kepler support, new macrobatch and serialization enhancements) and examples, along with an expanded Model Zoo to help users get started with their use cases. New storage format (docs) and data loader for loading datasets that do not fit in…

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#Release Notes #Scene Recognition

Jan 29, 2016   |   Stewart Hall

Faster Training in neon with Multiple GPUs on the Nervana Cloud

The Nervana Cloud provides unprecedented performance, ease of use, and the ability to apply deep learning to a large range of machine learning problems. With modern networks taking days, weeks or even months to train, performance is one of our fundamental goals. GPUs allow us to greatly improve performance by parallelizing convolution and matrix multiply…

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#Intel DL Cloud & Systems #neon

Jan 19, 2016   |   Scott Leishman

neon v1.1.5 released!

Highlights from this release include:  * CUDA kernels for lookuptable layer. This results in a 4x speedup for our sentiment analysis model example * support for determinstic Conv layer updates * custom dataset walkthrough utilizing bAbI data * reduced number of threads in deep reduction EW kernels [#171] * additional (de)serialization routines [#106] * CPU…

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#Release Notes

pre trained deep learning models

Dec 29, 2015   |   Tambet Matiisen

Guest Post (Part II): Deep Reinforcement Learning with Neon

This is part 2 of a blog series on deep reinforcement learning. See part 1 “Demystifying Deep Reinforcement Learning” for an introduction to the topic. The first time we read DeepMind’s paper “Playing Atari with Deep Reinforcement Learning” in our research group, we immediately knew that we wanted to replicate this incredible result. It was…

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#neon #Reinforcement Learning