Jul 16, 2016 | Hanlin Tang
Intel Nervana is excited to share a series of short Nervana videos and accompanying exercises to learn how to build deep learning models with neon, our deep learning framework. We start with a basic introduction into deep learning concepts, provide an overview of the neon framework, and discuss key neon concepts such as loading data…
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The simplicity of neon’s Python-like syntax makes it an extraordinarily powerful, out-of-the-box tool for anyone from the novice, to the experienced scientist.
Assembler-level optimization, multi-GPU support, optimized data-loading, and the Winograd algorithm all add-up to stellar performance.
neon’s nviz utility helps you track progress and better understand the deep learning process with inspired visuals and histograms.
neon™ comes with its own Model Zoo featuring pre-trained weights and example scripts for state-of-the-art models—the ones you see below are just a few.
ImageNet
- Googlenet
- VGG
- Alexnet
CIFAR-10
- Deep Residual Network
- All CNN
bAbI (Question Answering)
-GRU/LSTM model
Sentiment classification
-LSTM (IMDB dataset)
A fast 3-D convolution network (C3D) based model for activity recognition using neon on the UCF-101 dataset.
Neon’s implementation of Baidu’s "Deep Speech 2" model for speech recognition trained on audio-books from the Librispeech corpus.
Spectrogram (top left), raw audio (top right), and FFT spectrum (bottom).
A LSTM-based model constructed in neon to perform sentiment analysis on live Twitter stream.
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