Upcoming Webinar

Deep Learning for Computer Vision

Date:
April 20, 2017
Session 1 Time:
9:00 a.m. U.S. EDT/ 2:00 p.m. BST/ 3:00 p.m. CEST
Session 2 Time:
2:00 p.m. U.S. EDT/ 7:00 p.m. GMT/ 8:00 p.m. CEST
Session 3 Time:
9:00 p.m. U.S. EDT/ April 21, 2017 11:00 a.m. AEST; 1:00 p.m. NZST

Overview

While deep learning can achieve state-of-the-art accuracy for object recognition and object detection, it can be difficult to train, evaluate and compare deep learning models. Deep learning also requires a significant amount of data and computational resources.

In this webinar, we will explore how MATLAB addresses the most common deep learning challenges and gain insight into the procedure for training accurate deep learning models. We will cover new capabilities for deep learning and computer vision for object recognition and object detection.

Highlights

We will use real-world examples to demonstrate:

  • Accessing and managing large sets of images
  • Using visualization to gain insight into the training process
  • Leveraging pre-trained networks to perform new recognition tasks using transfer learning
  • Speeding up the training process using GPUs and Parallel Computing Toolbox

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.

About the Presenters

Johanna Pingel joined the MathWorks team in 2013, specializing in Image Processing and Computer Vision applications with MATLAB. She has a M.S. degree from Rensselaer Polytechnic Institute and a B.A. degree from Carnegie Mellon University. She has been working in the Computer Vision application space for over 5 years, with a focus on object detection and tracking.

Avinash Nehemiah works on computer vision applications in technical marketing at MathWorks. Prior to joining MathWorks he spent 7 years as an algorithm developer and researcher designing computer vision algorithms for hospital safety and video surveillance. He holds an MSEE degree from Carnegie Mellon University.

Product Focus

  • Neural Network Toolbox
  • Computer Vision System Toolbox
  • Statistics and Machine Learning Toolbox