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Intel® AI Builders
White Paper
Intel® AI Builders - White Paper
This post illustrates how with the help of Intel® artificial intelligence and machine learning, Ziva Dynamics is transforming how filmmakers create visual effects.
Categories: 
By Verticals - Media and Entertainment | Compute - Intel® Xeon® Processors | Framework Optimizations - Torch/PyTorch | Software Libraries - Intel® Math Kernel Library (Intel® MKL), Intel® Data Analytics Acceleration Library (Intel® DAAL), Intel® Distribution for Python

This post illustrates how with the help of Intel® artificial intelligence and machine learning, Ziva Dynamics is transforming how filmmakers create visual effects.

Intel® AI Builders
White Paper
Intel® AI Builders - White Paper
This post illustrates how the OpenVINO™ Model Server extends workloads across Intel® hardware (including accelerators) and maximizes performance across computer vision accelerators—CPUs, integrated GPUs, Intel Movidius VPUs, and Intel FPGAs
Categories: 
Compute - Intel® Xeon® Processors, Intel® Stratix® 10 FPGA, Intel® Movidius™ Myriad™ X VPU | Framework Optimizations - TensorFlow, Caffe, MXNet | Tools - Intel® OpenVINO | Topology - ResNet50 | Workload - Inference, Batch Learning

This post illustrates how the OpenVINO™ Model Server extends workloads across Intel® hardware (including accelerators) and maximizes performance across computer vision accelerators—CPUs, integrated GPUs, Intel Movidius VPUs, and Intel FPGAs

Intel® AI Builders
White Paper
Intel® AI Builders - White Paper
This document illustrates how, in addition to being a common platform for inference workloads, Intel Xeon Scalable processor is a competitive platform for both training and inference.
Categories: 
By Verticals - Other | Compute - Intel® Xeon® Processors | Framework Optimizations - MXNet | Software Libraries - Intel® Math Kernel Library (Intel® MKL), Intel® Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) | Workload - Inference, Training

This document illustrates how, in addition to being a common platform for inference workloads, Intel Xeon Scalable processor is a competitive platform for both training and inference.

Intel® AI Builders
White Paper
Intel® AI Builders - White Paper
This document explores deep learning systems for image recognition in health and life sciences (HLS) and how Intel’s portfolio of products for artificial intelligence (AI) is helping make HLS AI solutions a reality.
Categories: 
By Use Case - Image Recognition/Detection/ Classification, Analytics | By Verticals - Healthcare, Life Sciences | Compute - Intel® Xeon® Processors | Framework Optimizations - TensorFlow, Caffe | Software Libraries - Intel® Math Kernel Library (Intel® MKL) | Workload - Supervised Learning, Inference, Training, Batch Learning

This document explores deep learning systems for image recognition in health and life sciences (HLS) and how Intel’s portfolio of products for artificial intelligence (AI) is helping make HLS AI solutions a reality.

Intel® AI Builders
White Paper
Intel® AI Builders - White Paper
This document describes the setup, installation and procedure to run distributed Deep Learning training and inference using TensorFlow with Uber Horovod library on Intel® Xeon® based infrastructure. The steps required to run the benchmark can vary depending on the user’s environment. In case of a large cluster with the order of hundreds or thousands of nodes, we provide sample scripts that use the SLURM scheduler. Alternatively, we also list out steps for smaller systems that may not have such a scheduler configured. Furthermore, we also provide scripts to build a singularity image for ease of deployment.
Categories: 
Compute - Intel® Xeon® Processors | Framework Optimizations - TensorFlow, Keras, Torch/PyTorch | Software Libraries - Intel® Math Kernel Library (Intel® MKL), Intel® Distribution for Python | Topology - ResNet50 | Workload - Inference, Training

This document describes the setup, installation and procedure to run distributed Deep Learning training and inference using TensorFlow with Uber Horovod library on Intel® Xeon® based infrastructure. The steps required to run the benchmark can vary depending on the user’s environment. In case of a large cluster with the order of hundreds or thousands of nodes, we provide sample scripts that use the SLURM scheduler. Alternatively, we also list out steps for smaller systems that may not have such a scheduler configured. Furthermore, we also provide scripts to build a singularity image for ease of deployment.

Intel® AI Builders
White Paper
Intel® AI Builders - White Paper
This White Paper illustrates how Julia Computing and Intel are powering the next big wave in Artificial Intelligence with high performance, advanced Machine Learning and Deep Learning based on Intel® architecture.
Categories: 
By Use Case - Big Data, Analytics | By Verticals - Healthcare, Financial Services, Pharma and Biotech | Compute - Intel® Xeon® Processors | Framework Optimizations - TensorFlow, MXNet

This White Paper illustrates how Julia Computing and Intel are powering the next big wave in Artificial Intelligence with high performance, advanced Machine Learning and Deep Learning based on Intel® architecture.

Intel® AI Builders
Solution Brief
Intel® AI Builders - Solution Brief
This solution brief highlights Ammune Defense Shield, an artificial intelligence (AI)-based self-learning cyberdefense system from L7 Defense* that reacts quickly to protect against extgeneration scripted or artificial intelligence (AI)-based cyberattacks. These attacks can evolve faster than humans can respond, but Ammune starts quickly and changes its defenses to match changes in the attack.
Categories: 
By Use Case - Anomaly Detection, Analytics | By Verticals - Other, Government | Compute - Intel® Xeon® Processors | Workload - Unsupervised Learning

This solution brief highlights Ammune Defense Shield, an artificial intelligence (AI)-based self-learning cyberdefense system from L7 Defense* that reacts quickly to protect against extgeneration scripted or artificial intelligence (AI)-based cyberattacks. These attacks can evolve faster than humans can respond, but Ammune starts quickly and changes its defenses to match changes in the attack.

Intel® AI Builders
White Paper
Intel® AI Builders - White Paper
The purpose of this article is to illustrate how enterprises can start implementing AI projects right away in their existing data centers with the flexibility that Intel Xeon processors offer and with powerful tools Lenovo provides to realize the benefits of AI.
Categories: 
By Use Case - Big Data, Analytics | By Verticals - Other | Compute - Intel® Xeon® Processors | Framework Optimizations - TensorFlow, Caffe, neon™

The purpose of this article is to illustrate how enterprises can start implementing AI projects right away in their existing data centers with the flexibility that Intel Xeon processors offer and with powerful tools Lenovo provides to realize the benefits of AI.

Intel® AI Builders
White Paper
Intel® AI Builders - White Paper
This paper introduces an image-based house recommendation system that was built between MLSListings* and Intel® using BigDL1 on Microsoft Azure*. Using Intel’s BigDL distributed deep learning framework, the recommendation system is designed to play a role in the home buying experience through efficient index and query operations among millions of house images.
Categories: 
By Use Case - Image Recognition/Detection/ Classification, Object Recognition/Detection/ Classification, Computer Vision (CV), Analytics | By Verticals - Retail | Framework Optimizations - Caffe, BigDL | Topology - SSD-VGG16 | Workload - Supervised Learning, Inference

This paper introduces an image-based house recommendation system that was built between MLSListings* and Intel® using BigDL1 on Microsoft Azure*. Using Intel’s BigDL distributed deep learning framework, the recommendation system is designed to play a role in the home buying experience through efficient index and query operations among millions of house images.

Intel® AI Builders
White Paper
Intel® AI Builders - White Paper
This is an educational white paper on transfer learning, showcasing how existing deep learning models can be easily and flexibly customized to solve new problems. One of the biggest challenges with deep learning is the large number of labeled data points that are required to train the deep learning models to sufficient accuracy.
Categories: 
By Use Case - Image Recognition/Detection/ Classification, Analytics | By Verticals - Media, Healthcare | Compute - Intel® Xeon® Processors | Framework Optimizations - TensorFlow | Topology - ResNet50, InceptionV3 | Workload - Unsupervised Learning, Inference

This is an educational white paper on transfer learning, showcasing how existing deep learning models can be easily and flexibly customized to solve new problems. One of the biggest challenges with deep learning is the large number of labeled data points that are required to train the deep learning models to sufficient accuracy.