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QuikFynd Advanced Data Search

Tame fragmented data with QuikFynd advanced data search using the OpenVINO™ toolkit

ORBO* AI Harnessing the OpenVINO™ Toolkit to Revolutionize Image Upscaling

发布时间:2019 年 6 月 25 日

The primary focus of Orbo has been to bring visual enhancements using a real-time, super resolution technology for video and images.

Optimizing Face Recognition Inference on Intel® Xeon® Scalable Processors

发布时间:2019 年 6 月 24 日

A face recognition inference experiment using Intel® Xeon® scalable processors showing the AI power of face recognition applications.

Facilitating AI Power in Smart Retail Using Intel® Optimization for Caffe* on Intel® Xeon® Scalable Processors

发布时间:2019 年 6 月 24 日

This article shows inference performance gains can be achieved using Intel® Optimization for Caffe* compared to open BVLC Caffe* on Intel® Xeon® scala

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Graph Attention Networks Using Intel® Optimization for Tensorflow* on Intel® Xeon® Scalable Processors

发布时间:2019 年 6 月 21 日

The adjacency matrix in real world graph is sparse. CPU systems without discrete hardware accelerators provide competitive training

AI & Security Innovations Help Developers Preserve Privacy While Delivering Insight

发布时间:2019 年 6 月 18 日作者:Jim Gordon

Homomorphic encryption opens up new possibilities by enabling computer calculations on encrypted information without decrypting it.

Model Serving with Analytics Zoo

上次更新时间:2019 年 6 月 17 日视频时长:18 分钟

The tutorial illustrates how to deploy a trained deep learning model with Analytics Zoo for inference or serving. Analytics Zoo is a unified analytics and AI platform, with Apache Spark*, BigDL, TensorFlow*, Keras.

TensorFlow* on Apache Spark* with Analytics Zoo

上次更新时间:2019 年 6 月 14 日视频时长:15 分钟

The tutorial illustrates TensorFlow* on Apache Spark* with Analytics Zoo. Analytics Zoo is a unified analytics and AI platform, with Apache Spark, BigDL, TensorFlow, Keras.

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Performance Boosting in Seldon

发布时间:2019 年 6 月 11 日作者:Dariusz T.

The Seldon Core open source machine learning deployment platform facilitates management of inference pipelines

Getting Started with the Intel® NCS2 on Linux*

上次更新时间:2019 年 6 月 6 日视频时长:3 分钟

How to get started with the Intel® Neural Compute Stick 2 (Intel® NCS2) on Linux.

Analytics Zoo Introduction

上次更新时间:2019 年 6 月 4 日视频时长:24 分钟

Analytics Zoo is a unified analytics and AI platform, with Apache Spark*, BigDL, Tensorflow*, Keras.

Use Analytics Zoo Keras style API to solve classification problem

上次更新时间:2019 年 5 月 31 日视频时长:11 分钟

The tutorial shows Analytics Zoo Keras style API for classification problem. Analytics Zoo is a unified analytics and AI platform, with Apache Spark*, BigDL, Tensorflow*, Keras.

Use Analytics Zoo Keras Style API to Solve Regression Problems

上次更新时间:2019 年 5 月 22 日视频时长:13 分钟

The tutorial shows Analytics Zoo Keras style API for regression problems. Analytics Zoo is a unified analytics and AI platform, with Apache Spark*, BigDL, TensorFlow*, Keras.

Windows® 10 May 2019 Update for Machine Learning Acceleration on Intel® Integrated Graphics

发布时间:2019 年 5 月 17 日作者:Gokul Tonpe

Intel's earlier post in May 2018 introduced the Windows ML API and the DirectML API implementation on Intel® hardware via the DirectX 12 DirectCompute

Windows® 10 2019 年 5 月更新有助于加速英特尔® 集成显卡上的机器学习

发布时间:2019 年 5 月 17 日作者:Gokul Tonpe

英特尔 2018 年 5 月发表的博文介绍了通过 DirectX 12 DirectCompute 在英特尔® 硬件上实施的 Windows ML API 和 DirectML API

Intel® CPU Outperforms NVIDIA* GPU on ResNet-50 Deep Learning Inference

发布时间:2019 年 5 月 13 日作者:Haihao Shen

Intel Xeon processor outperforms NVidia's best GPUs on ResNet-50.

Introduction to Natural Language Processing (NLP) Architect

上次更新时间:2019 年 5 月 13 日视频时长:52 分钟

This webinar focuses on introducing the audience to Natural Language Processing (NLP) Architect, a Python* library from the Intel® AI Lab for exploring the state-of-the-art deep learning topologies.

Introduction to the Intel® Distribution of OpenVINO™ Toolkit and Windows Machine Learning*

上次更新时间:2019 年 5 月 13 日视频时长:1 分钟

In this webinar you will learn how real-time inference on the PC for visual workloads such as object detection, recognition, and tracking are now easily developed with Intel® Distribution of OpenVINO™ Toolkit and Windows Machine Learning* API.

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Build Faster AI Solutions with the Intel-Optimized ONNX* Runtime

发布时间:2019 年 5 月 8 日作者:Andy Vargas

Intel and Microsoft* are co-engineering tools based on the open source ONNX Runtime to take advantage of the latest AI-boosting features from Intel.

Video Series: Hands-On AI | Part 1: Introduction

上次更新时间:2019 年 5 月 2 日视频时长:1 分钟

In this video series, learn how image data preprocessing can be used to clean and augment transformations on a dataset and why it matters.

 

Video Series: Hands-On AI | Part 2: Cleaning Transformations

上次更新时间:2019 年 5 月 2 日视频时长:3 分钟

Explore the concepts of data cleaning, which is an important aspect of data preprocessing for machine learning workloads. Get an introduction to common Keras (an open source neural network library) transformations like rescaling, gray scaling, sample wise centering, sample wise normalization, feature wise centering, and feature-wise normalization.

Video Series: Hands-On AI | Part 3: Augmentation Transformations

上次更新时间:2019 年 5 月 2 日视频时长:2 分钟

Learn some of the transformations used for data augmentation, an important aspect when training a model to work in varied environments. Explore rotation, horizontal shift, vertical shift, shearing, zoom, and horizontal and vertical flip transformations with examples.

Video Series: Hands-On AI | Part 4: Augmenting and Cleaning in Practice

上次更新时间:2019 年 5 月 2 日视频时长:2 分钟

Combine the techniques from Part 3 in a practical way to strengthen the dataset. Understand how the Keras code is used to augment a dataset in a Jupyter* Notebook running on the Intel® DevCloud.

Video Series: Hands-On AI | Part 5: Conclusion

上次更新时间:2019 年 5 月 2 日视频时长:1 分钟

The combination of these videos provides you with knowledge on how image data preprocessing can be used for cleaning and augmenting transformations on a dataset and why it matters.

Detecting Acute Lymphoblastic Leukemia Using Caffe*, OpenVINO™ and Intel® Neural Compute Stick 2: Part 1

First part of a series that will take you through my experience building a custom classifier with Caffe* that should be able to detect AML/ALL.

“英特尔杯”中国研究生人工智能创新大赛

大赛新闻

中国研究生人工智能创新大赛常见问题   发布时间:2019-05-17
请首先仔细阅读 “英特尔杯”第一届中国研究生人工智能大赛的参赛条款和参赛条件以及比赛规则,关于资格,比赛流程...

Intel® DevCloud Published Datasets

发布时间:2019 年 4 月 26 日

The AI datasets described here were cleaned and preprocessed for use on the Intel® DevCloud. Includes descriptions, usage examples, keywords, and more

利用 OpenVINO™ 进行基于 INT8 量化的推理优化

发布时间:2019 年 4 月 24 日

作者:Qian, Caihong

前言

OpenVINO™ 是英特尔推出的一套免费的开发套件,目的是帮助开发者和数据科学家们加速他们在视觉计算以及深度学习的推理和部署方面的工作。OpenVINO™ 从 2018 R4 发行版开始,已经推出支持 INT8 数据类型的相应工具...

Maximize Performance of Intel® Optimization of PyTorch*/Caffe2* Framework on CPU

发布时间:2019 年 4 月 18 日作者:Jing X.

This article describes what you need to consider in order to get a satisfying performance with PyTorch, with examples.

Distributed Training of Deep Learning Models with PyTorch*

上次更新时间:2019 年 4 月 18 日

The motive of this article is to demonstrate the idea of distributed computing in the context of training large scale deep learning (DL) models....

Object Detection: A Comparison of Performance of Deep Learning Models on Edge Using Intel® Movidius™ Neural Compute Stick and Raspberry PI* 3

上次更新时间:2019 年 4 月 18 日

Vehicle Detection involves finding whether there is vehicle present or not secondly which type of vehicle is present and how many vehicles are...

Towards Privacy-Preserving Machine Learning

上次更新时间:2019 年 4 月 18 日

When Artificial Intelligence involves some type of sensitive data, the problem is how to maintain the data privacy and security. This problem...

Implementing Attention Models in PyTorch*

上次更新时间:2019 年 4 月 18 日

Recurrent Neural Networks have been the recent state-of-the-art methods for various problems whose available data is sequential in nature. Adding...

Introduction to Reinforcement Learning Coach

上次更新时间:2019 年 4 月 16 日视频时长:50 分钟

Introducing Reinforcement Learning (RL) Coach.

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