Deep Learning for Computer Vision

Presented By:
Matthew Hagen, Data Scientist at Home Depot
Time and Location

Apr 18, 2020 at 9:00am - 3:00pm EDT

Cobb Galleria Center

Workshop Agenda

Part 1: Introduction and Tutorial Infrastructure

We will review the outline of the course and ensure all attendees have access to the course slides and can install all necessary tools. e.g. jupyter notebook, python libraries, etc.

30 min


Part 2: Traditional Computer Vision

In this session, we will cover traditional computer vision techniques including Haar, SIFT, and HOG features.

We will also introduce the OpenCV frameworks as well as Viola-Jones facial detection.

30 min


Break 15 min


Part 3: Convolutional Neural Networks

Review the most common operations of CNN Architectures, pooling, convolutions and fully connected layers.

This session will then cover popular CNNs used in the industry including VGGNet, Inception, and Resnet.

75 min


Lunch Break 60 min


Part 4: Object Detection

This session will introduce object detection with popular two-stage approaches: R-CNN, Fast R-CNN, and Faster R-CNN.

We will then cover faster single stage approaches including SSD and YOLO.

75 min


Break 15 min


Part 5: Object Segmentation

We will discuss the most popular segmentation approaches using fully convolutional networks: FCN, U-Net, Mask-RCNN and PSPNet.

75 min


Break 15 min


Part 6: Advanced applications

This final session will discuss generative approaches by using deep learning to generate synthetic images with GANs.

This will cover popular networks such as CycleGAN and Pix2pix.

60 min




Instructor Bio

Coming soon..

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