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Installing Intel® Performance Libraries and Intel® Distribution for Python* Using APT Repository

Published on February 16, 2017, updated July 9, 2019By Gergana S.

Installation guide for the Intel® Performance Libraries and Intel® Distribution for Python through APT repos.

Installing Intel® Performance Libraries and Intel® Distribution for Python* via popular package managers

Published on January 19, 2017, updated July 8, 2019By Gergana S.

Select Package for Download Instructions

Developers can now easily access the following Intel® Software Development Tools through several Linux* and Windows* package managers at no cost:

Intel® Math Kernel Library Intel® Threading Building...

How to Get Intel® Math Kernel Library, Intel® Integrated Performance Primitives, or Intel® Data Analytics Acceleration Library

Published on June 4, 2017, updated July 3, 2019By Ying H.

This page provides links to the current ways to get the Intel® Performance Libraries: Intel® Math Kernel Library (Intel® MKL), Intel® Integrated Performance Primitives (Intel® IPP), or Intel® Data Analytics Acceleration Library (Intel® DAAL)....

Installing Intel® Performance Libraries and Intel® Distribution for Python* Using YUM Repository

Published on February 16, 2017, updated July 1, 2019By Gergana S.

This page provides general installation and support notes about the Community forum supported Intel® Performance Libraries and Intel® Distribution for Python* as they are distributed via the YUM repositories described below.


Accelerating Document Classification (Training) using Intel® Optimization for TensorFlow* on Intel® Xeon® Scalable Processors

Published on June 27, 2019


Most of the success of modern AI, especially deep learning algorithms, is due to its impressive results in image classification where near human-level has been observed. This capability can be used for document authentication which is a...

Intel® Distribution for Python* Release Notes and New Features

Published on August 11, 2016, updated May 23, 2019By David L.

This page provides the current Release Notes for the Intel® Distribution for Python*. The notes are categorized by year, from newest to oldest, with individual releases listed within each year.

Click a version to expand it into a summary...

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Multi-Planar Spatial-ConvNet for Segmentation of Brain Tumors

Last updated: December 3, 2018

Subhashis Banerjee

This presentation introduces a new deep learning method for the automatic delineation and segmentation of brain tumors from multisequence magnetic resonance imaging (MRI). It includes a radiomic model to predict the overall survival based on the features extracted from the segmented volume of interest (VOI). Also included is an encoder-decoder-type convolutional neural network (ConvNet) model for pixel-wise segmentation of the tumor along three anatomical planes (axial, sagittal, and coronal) at the slice level.

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AI for Reconstruction – Structural Area Validator Using Deep Learning

Last updated: December 3, 2018

Kshitiz Rimal

In April 2015, a massive earthquake hit rural areas of Nepal and destroyed many homes. Many victims were able to reconstruct their houses aided by the distribution of funds from the government program. This project identifies, detects, and labels the houses that were not built under Nepalese government standards.

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Bengali Digit Recognition from Real-World Images

Last updated: December 3, 2018

Debdyut Hajra

Extraction of text from images has important potential applications, such as visual aids for the blind, guides for tourists, and more. Such a system is still far from reality for many Indian languages like Bengali. This project deals with the development of a robust image preprocessing pipeline to effectively extract isolated digits from an image, and then translate the digits to the desired format. The project also explores how to build an efficient deep learning model to predict the digits by training on standard publicly available datasets.

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Inceptionism and Residualism in the Classification of Breast Fine-Needle Aspiration Cytology Cell Samples

Last updated: December 3, 2018

Amartya Ranjan Saikia

Fine needle aspiration cytology (FNAC) entails using a narrow gauge needle to collect a sample of a lesion for microscopic examination. It allows a minimally invasive, rapid diagnosis of tissue but does not preserve its histological architecture. This project presents a comparison of the various fine-tuned transfer learned classification approaches based on deep convolutional neural networks (CNN) for diagnosing the cell samples.

Intel® Distribution for Python* Known Issues

Published on June 2, 2016, updated November 14, 2018By Gergana S.

  Intel Distribution for Python 2019 and 2018

Intel® Parallel Studio 2019.1 Intel Python cannot find libiomp on macOS*

Intel® Parallel Studio 2019.1 installations of python on macOS* do not correctly place...

Visual Serving of Thin & Small Objects Using Learning Methods

Last updated: September 6, 2018

Hanz Cuevas Velasquez

Automating rose pruning is an unexplored area that involves different computer vision problems because of the complex nature of the rose bushes. This research identifies three main steps that a robot should follow to perform this task and uses a convolutional neural network architecture and reinforcement learning:

  • Plant modelling to segment the branches
  • Object detection to detect the eye-buds on the bush
  • Visual surveying to make a robotic arm approach the branches and eye-buds