JOIN SHPCP   |   EVENTS   |  NEWS  |  LOGIN

Log in
As a charitable service-based nonprofit organization (NPO) coordinating individuals, businesses, academia and governments with interests in High Technology, Big Data and Cybersecurity, we bridge the global digital divide by providing supercomputing access, applied research, training, tools and other digital incentives “to empower the underserved and disadvantaged.”

Parallel Computing & Machine Learning with MATLAB at University of Houston

  • 20 Mar 2019
  • University of Houston Health 1 Building

Registration

Parallel Computing & Machine Learning with MATLAB at University of Houston

Location Venue Start Date End Date
University of Houston Health 1 Building Room 186 20 Mar 2019 - 1:00 PM 20 Mar 2019 - 4:00 PM

Overview

Please join MathWorks Engineers as they demonstrate strategies and techniques for Parallel Computing & Machine Learning in MATLAB.

12:45 p.m.  Registration

Parallel and GPU computing with MATLAB (1:00p.m.-2:30p.m.)

In this session you will learn how to boost the execution speed of computationally and data-intensive problems using MATLAB and the Parallel Computing Toolbox. We will introduce and demonstrate the high-level programming constructs that allow you to easily create parallel MATLAB applications without low-level programming.

Highlights include:

  • Toolboxes with built-in algorithms for parallel computing
  • Creating parallel applications to speed up independent tasks
  • Scaling up to computer clusters, grid environments or clouds
  • Employing GPUs to speed up your computations

Demystifying Deep Learning: A Practical Approach in MATLAB (2:30 p.m.-4:00 p.m.)

In this session, we’ll demonstrate new MATLAB features that simplify these tasks and eliminate the low-level programming. In doing so, we’ll decipher practical knowledge of the domain of deep learning. We’ll build and train neural networks that recognize handwriting, classify food in a scene, and figure out the drivable area in a city environment.

Highlights include:

  • Manage extremely large sets of images
  • Visualize networks and gain insight into the black box nature of deep networks
  • Perform classification and pixel-level semantic segmentation on images
  • Import training data sets from networks such as GoogLeNet and ResNet
  • Import and use pre-trained models from TensorFlow and Caffe
  • Speed up network training with parallel computing on a cluster
  • Automate manual effort required to label ground truth



FOUNDATION SPONSORS

           

                 



PREMIERE SPONSORS

                         



  


ENTERPRISE SPONSORS