Cuesys Infotech :: Consulting | Corporate Training

Program Objectives
Program Overview

By attending Convolutional Neural Networks workshop, Participants will:

Understand how to build a convolutional neural network, including recent variations such as residual networks.
Know how to apply convolutional networks to visual detection and recognition tasks.
Know to use neural style transfer to generate art.
Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

This Convolutional Neural Networks training course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

COURSE AGENDA

Foundations of Convolutional Neural Networks

Deep convolutional models: case studies

Why look at case studies?
Classic Networks
ResNets
Why ResNets Work
Networks in Networks and 1x1 Convolutions
Inception Network Motivation
Inception Network
Using Open-Source Implementation4m
Transfer Learning
Data Augmentation
State of Computer Vision

Object detection

Object Localization
Landmark Detection
Object Detection
Convolutional Implementation of Sliding Windows
Bounding Box Predictions
Intersection Over Union
Non-max Suppression
Anchor Boxes
YOLO Algorithm
(Optional) Region Proposals

Special applications: Face recognition & Neural style transfer

What is face recognition?
One Shot Learning
Siamese Network
Triplet Loss
Face Verification and Binary Classification
What is neural style transfer?
What are deep ConvNets learning?
Cost Function
Content Cost Function
Style Cost Function
1D and 3D Generalizations