|Image Recognition||Classify image(s) with high accuracy||ResNet50 (I&T)
|Object Detection & Localization||Locate and classify object(s) in image||SSD-VGG16 (I&T)||SSD (I)||SSD-VGG16 (I&T)
|Speech Recognition||Convert speech to text||Deep Speech 2 (I)|
|Language Translation||Translation from one language to another||GNMT (I)||NMT (I)
|Recommender Systems||Predicts the rating or preference a user would give an item||Wide & Deep (I)|
|Generative Adversarial Networks||Neural networks that generate data mimicking some distribution||DCGAN (I)|
|Reinforcement Learning||The use of actions and results to learn how to behave in an environment||A3C (I)|
This Python*-based deep learning framework is designed for ease of use and extensibility on modern deep neural networks and has been optimized for use on Intel® Xeon® processors.
The open-source, deep learning framework MXNet* includes built-in support for the Intel® Math Kernel Library (Intel® MKL) and optimizations for Intel® Advanced Vector Extensions 2 (Intel® AVX2) and Intel® Advanced Vector Extension 512 (Intel® AVX-512) instructions.
Based on Python* and optimized for Intel® architecture, Intel’s innovative neon™ framework for deep learning is designed for ease of use and extensibility on modern deep neural networks.
The Intel® Optimization for Caffe* provides improved performance for of the most popular frameworks when running on Intel® Xeon® processors.
Theano*, a numerical computation library for Python, has been optimized for Intel® architecture and enables Intel® Math Kernel Library (Intel® MKL) functions.