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How to Get Started Developing for Automated Driving

Published on December 13, 2016, updated December 12, 2017

From safe roads to enjoyable commutes, automated driving is poised to change lives and society for the better. As the car moves to the center of the...

CIFAR-10 Classification using Intel® Optimization for TensorFlow*

Published on December 12, 2017

Abstract This work demonstrates the experiments to train and test the deep learning AlexNet* topology with the Intel® Optimization for TensorFlow*...

Power System Infrastructure Monitoring Using Deep Learning on Intel® Architecture

Published on July 14, 2017, updated December 11, 2017

This paper evaluates the performance of Intel® Xeon® processor powered machines for running deep learning on the GoogleNet* topology (Inception* v3...

Finding Missing Kids through Code

This article describes how Intel is contributing to the facial recognition component of Child Finder Service*. We hope that people from a broad range...

Manufacturing Package Fault Detection Using Deep Learning

Executive Summary Intel's Software and Services Group engineers recently worked with assembly and test factory engineers on a proof of concept...

AI News: December 2017

Last updated: December 7, 2017Video length: 1 min

In this episode you will get a look at deep neural networks and how they are being used to identify unattended baggage for new security. How deep learning could be used to discover crypto-currency trader sentiment. You'll also learn what Intel student ambassador Panuwat is doing in AI research.

Intel® Distribution for Python* versus Non-Optimized Python: Breast Cancer Classification

Published on December 6, 2017

Abstract This case study compares the performance of Intel® Distribution for Python* to that of non-optimized Python using a breast cancer...

Explore Unity Technologies ML-Agents* Exclusively on Intel® Architecture

This article describes how to install and run Unity Technologies ML-Agents* in CPU-only environments. It demonstrates how to: train and run the ML-...

Automatic Defect Inspection Using Deep Learning for Solar Farm

Executive Summary Intel's Software and Services Group (SSG) engineers recently collaborated with Intel's New Technology Group (NTG) to launch a...

Use TensorFlow* for Deep Learning Training & Testing on a Single-Node Intel® Xeon® Scalable Processor

Introduction This document provides step-by-step instructions on how to train and test a single-node Intel® Xeon® Scalable processor platform system...

MobileNets on Intel® Movidius™ Neural Compute Stick and Raspberry Pi 3

Introduction Deep Learning at the edge gives innovative developers across the globe the opportunity to create architecture and devices...

Using BigDL to Build Image Similarity-Based House Recommendations

Overview This paper introduces an image-based house recommendation system that was built between MLSListings* and Intel® using BigDL1 on Microsoft...

Installing and Building MXNet with Intel® MKL

The latest version of MXNet includes built-in support for the Intel® Math Kernel Library (Intel® MKL) 2018. The latest version of the Intel MKL...

Intel® Processors for Deep Learning Training

On November 7, 2017, UC Berkeley, U-Texas, and UC Davis researchers published their results training ResNet-50* in a record time (as of the time of...

Getting started with the Intel® AI Devcloud

Hello all! Happily, this article refers to the newly accessible Intel® AI Devcloud! If you don't have access, sign up for it now.  If you don't know...

AI News: November 2017

Last updated: November 2, 2017Video length: 1 min

In this episode you'll get an inside look at building a large-scale image-feature extraction framework with BigDL, You'll learn how to get you can get Free DevCloud access, and lastly you'll get an early look at CES 2018.

This is the 1 year anniversary of AI News, it's been fun, and we're looking forward to continuing to bringing you more episodes in the year to come!

Hands-On AI Part 23: Deep Learning for Music Generation 2—Implementing the Model

Published on October 30, 2017

This article used BachBot as a case study in discussing the considerations of building a creative deep learning model. Specifically, this article...

Hands-On AI Part 22: Deep Learning for Music Generation 1—Choosing a Model and Data Preprocessing

Published on October 29, 2017

his article discussed some of the early steps in implementing a deep learning model using BachBot as an example. In particular, it discussed the...

Hands-On AI Part 21: Emotion-Based Music Transformation

Published on October 28, 2017

In this article we presented the core idea behind emotion-based music transformation—manipulation with position of a particular note on a scale...

Hands-On AI Part 20: Music Data Collection and Exploration

Published on October 27, 2017

A Tutorial Series for Software Developers, Data Scientists, and Data Center Managers This is the 20th article in the AI Developer Journey Tutorial...

Hands-On AI Part 19: Music Dataset Search

Published on October 27, 2017

The search for the music data itself was not a very time-consuming task. However, a lot of careful consideration was required to come up with...

Hands-On AI Part 17: Emotion Recognition from Images Baseline Model

Published on October 25, 2017

In this article, we will be building a baseline convolutional neural network (CNN) model that is able to perform emotion recognition from images....

Hands-On AI Part 18: Emotion Recognition from Images Model Tuning and Hyperparameters

Published on October 25, 2017

In this article, we will build an advanced CNN model for emotion recognition from images using the technique called transfer learning. Please read...

Hands-On AI Part 15: Overview of Convolutional Neural Networks for Image Classification

Published on October 24, 2017

In this article, we will provide a comprehensive theoretical overview of the convolutional neural networks (CNNs) and explain how they could be used...

Hands-On AI Part 16: Modern Deep Neural Network Architectures for Image Classification

Published on October 24, 2017

In this article, we will consider several powerful deep neural network architectures, such as AlexNet*, ZFNet*, VGG*, GoogLeNet*, and ResNet*, and...

Deep Learning Training and Testing on a Single Node Intel® Xeon® Scalable Processor System Using Intel® Optimized Caffe*

I. Introduction This document provides step-by-step instructions on how to train and test your trained single node Intel® Xeon® Scalable processor...

Deep Learning for Cryptocurrency Trading

A new potential use case of deep learning is the use of it to develop a Cryptocurrency Trader Sentiment Detector. I am currently developing a...

Part 20 | AI, IoT, and Voice as a Natural Interface

Last updated: October 16, 2017Video length: 36 min

Learn why voice is the natural choice of interface for IoT devices and why focusing on Open Source technologies empower developers.

Hands-On AI Part 14: Image Data Preprocessing and Augmentation

Published on October 13, 2017

In this article, we described an overview of the common techniques of image preprocessing such as scaling, normalization, rotation, shifting, and...

Hands-On AI Part 11: Image Dataset Search

Published on October 12, 2017

We wanted to find an emotion recognition model that used images to predict multiple negative and positive emotions. To this end, we gathered images...

Hands-On AI Part 12: Image Data Collection

Published on October 12, 2017

This article discusses the methods used for image data collection in the slideshow music project. Please refer to previous article 11 for information...

Hands-On AI Part 13: Image Data Exploration

Published on October 12, 2017

we divided our image database into neutral, negative, and positive emotion categories by using normative ratings of valence that ranged from 0 to 100...

AI News: October 2017

Last updated: October 5, 2017Video length: 2 min

In this episode you'll learn about the first ever Intel student ambassador forum, how Intel is helping deliver ‘Real-Time AI’ in Microsoft’s new accelerated deep learning platform and we announce the winners of Intel’s Kaggle competition!

Building Large-Scale Image Feature Extraction with BigDL at JD.com

This article shares the experience and lessons learned from Intel and JD teams in building a large-scale image feature extraction framework using...

Hands-On AI Part 10: Set Up a Portable Experimental Environment for Deep Learning with Docker*

Published on September 25, 2017

A Tutorial Series for Software Developers, Data Scientists, and Data Center Managers Here is a common scenario of why you should use a portable...

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