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

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

Last updated: May 2, 2019Video length: 1 min

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

Last updated: May 2, 2019Video length: 3 min

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

Last updated: May 2, 2019Video length: 2 min

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

Last updated: May 2, 2019Video length: 2 min

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

Last updated: May 2, 2019Video length: 1 min

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.

Intel® DevCloud Published Datasets

Published on April 26, 2019

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

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

Published on April 18, 2019By 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*

Last updated: April 18, 2019

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

Last updated: April 18, 2019

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

Last updated: April 18, 2019

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*

Last updated: April 18, 2019

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

Last updated: April 16, 2019Video length: 50 min

Introducing Reinforcement Learning (RL) Coach.

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Develop Advanced Analytics Solutions with AI at Scale Using Apache Spark* and Analytics Zoo

Published on April 15, 2019By Ziya M.

Analytics Zoo and BigDL on Intel® Xeon® processor-based platforms deliver deep learning Apache Spark* pipelines at scale.

Transitioning from Intel® Movidius™ Neural Compute SDK to Intel® Distribution of OpenVINO™ toolkit

This article provides guidance for transitioning from the NCSDK to the Intel® Distribution of OpenVINO™ toolkit.

Code Sample: Intel® AVX512-Deep Learning Boost: Intrinsic Functions

How developers can use to take advantage of the new Intel® AVX512-Deep Learning Boost (Intel® AVX512-DL Boost) instructions.

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Second Generation Intel® Xeon® Processor Scalable Family Technical Overview

New features and enhancements available in the second generation Intel® Xeon® processor Scalable family and how developers can take advantage of them

Intel and Facebook* collaborate to boost PyTorch* CPU performance

Intel's software optimization and 2nd generation Intel® Xeon® Scalable Processors with Intel® DL Boost® accelerate PyTorch's CPU performance

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Get Started with Intel® Neural Compute Stick 2

Getting started steps for the Intel® Neural Compute Stick 2 and the Intel® Distribution of the OpenVINO™ toolkit.

Getting Started with Intel® Optimization for PyTorch* on Second Generation Intel® Xeon® Scalable Processors

Accelerate deep learning PyTorch* code on second generation Intel® Xeon® Scalable processor with Intel® Deep Learning Boost.

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

In this article I will cover the steps required to create the dataset required to train the model using the network we defined in the last tutorial.

Reducing False Negatives in the Invasive Ductal Carcinoma Classifier

This project tries to trick the model by using very similar, but opposite class, images from a small set of testing data that we believe humans may...

Intel® Math Kernel Library Improved Small Matrix Performance Using Just-in-Time (JIT) Code Generation for Matrix Multiplication (GEMM)

Published on September 6, 2018, updated March 15, 2019By Gennady F.

    The most commonly used and performance-critical Intel® Math Kernel Library (Intel® MKL) functions are the general matrix multiply (GEMM)...

Getting to Know the Intel® Neural Compute Stick 2

Last updated: March 13, 2019Video length: 48 min

In this webinar you’ll get an overview of the Intel® Neural Compute Stick 2 (Intel® NCS 2), what it is good for, and see how easy it is to get started.

Acute Myeloid/Lymphoblastic Leukemia Data Augmentation

The AML/ALL Classifier Data Augmentation program applies filters to datasets and increases the amount of training and test data available to use.

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How AI is Helping Us Better Understand the Environment

Published on March 5, 2019

Success Story: Researchers use AI techniques to help understand ecosystems better to analyze the complex interactions and patterns in our environment.

Deep Learning with Analytic Zoo Optimizes Mastercard* Recommender AI Service

Introduces a joint initiative between Mastercard* and Intel in building users-items propensity models for a universal recommender AI service.

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Computer Vision Annotation Tool: A Universal Approach to Data Annotation

At Intel, one of the projects we’re undertaking research on is developing computer vision algorithms based on deep neural networks (DNNs) and how...

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Harness the Power of Data from Connected Things

Published on February 26, 2019

For software developers, the opportunity is clear: to accelerate business by harnessing the power of data generated by connected things.

Talroo* Uses Analytics Zoo and AWS* to Leverage Deep Learning for Job Recommendations

This project demonstrates how to leverage the natural language context analysis and recommender models of Analytics Zoo on Amazon Web Services (AWS*)

Detecting Invasive Ductal Carcinoma with Convolutional Neural Networks

This article, Machine Learning and Mammography, shows how existing deep learning technologies can be utilized to train artificial intelligence (AI)...

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Identify Plant Anatomy Using the Intel® Distribution of OpenVINO™ Toolkit

Use Case: Build a model to identify plant anatomy with the Intel® Distribution of OpenVINO™ Toolkit

Optimization Practice of Deep Learning Inference Deployment on Intel® Processors

Published on February 19, 2019

Optimize performance of inference service on CPUs and save computing resources using the iQIYI deep learning cloud platform

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