Article

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
Authored by Gennady F. (Blackbelt) Last updated on 07/05/2019 - 14:54
Blog post

Announcing the Intel® Distribution for Python* Beta

The Beta for Intel® Distribution for Python* 2017 has been available for 1 month and I wanted to share some of our experiences.

Authored by Robert C. (Intel) Last updated on 12/31/2018 - 16:12
Article

Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*

This article discusses machine learning and describes a machine learning method/algorithm called Naïve Bayes (NB) [2]. It also describes how to use Intel® Data Analytics Acceleration Library (Intel® DAAL) [3] to improve the performance of an NB algorithm.
Authored by Nguyen, Khang T (Intel) Last updated on 07/06/2019 - 16:40
Article

Set Up Intel® Software Optimization for Theano* and Supporting Tools

Get recipes for installing development tools and libraries on various platforms for the Python library.
Authored by Sunny G. (Intel) Last updated on 05/08/2018 - 10:50
Blog post

Track Reconstruction with Deep Learning at the CERN CMS Experiment

Connecting the Dots
Authored by Last updated on 12/12/2018 - 18:00
Blog post

Track Reconstruction with Deep Learning at the CERN CMS Experiment

This blog post is part of a series that describes my summer school project at CERN openlab.

Authored by Last updated on 03/21/2019 - 12:00
Article

Getting started with the Intel® AI Devcloud

Hello all! Happily, this article refers to the newly accessible Intel® AI Devcloud!

Authored by Karl Fezer (Intel) Last updated on 03/11/2019 - 13:17
Article

Brain Tumor Segmentation using Fully Convolutional Tiramisu Deep Learning Architecture

The aim of the work was to implement, train and evaluate the quality of automated brain tumor multi-label segmentation technique for Magnetic Resonance Imaging based on Tiramisu deep learning architecture.
Authored by Kocot, Szymon Last updated on 03/26/2019 - 16:20
Article

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.
Authored by Milton-Barker, Adam Last updated on 06/07/2019 - 16:47