Practical Deep Neural Networks AI :
Best Practices for Gradient Learning
WHEN
22 - 24 NOVEMBER 2021
WHERE
MS TEAMS
TIME
9.00AM - 1.00PM
RM 1,250 FOR PROFESSIONALS
10% Discount for Early Bird (until 22 November 2021) / Group / Students
CONTENT SUMMARY
INTRODUCTION
Deep neural network artificial intelligence (AI) has brought powerful pattern recognition capabilities to various applications in a broad span of industries. Setting up complex image interpretation and recognition software no longer requires deep expertise in machine vision feature selection. Instead, the technical challenge has been greatly simplified to the process of acquiring plenty of high-quality labeled image data and applying supervised gradient descent learning on popular architectures like the deep convolutional neural network using open source software frameworks like Tensorflow and Pytorch. While it is now easy to set up a deep neural network classifier within a few hours by following one of many tutorial instructions online, it remains challenging to ensure that the deep neural network is robustly well-trained for all kinds of data. If not well-configured, gradient learning often yields suboptimal classification and sometimes just fails to converge. This course focuses on the best practices in designing and configuring gradient learning for deep neural networks. We first introduce the methodology of gradient learning and backpropagation and highlight where gradient learning commonly fails. We review common training loss functions and regularization strategies which improve the convergence of gradient learning. With a good understanding of these fundamentals, we will study the motivation and implementation of input, weight and activation normalizations and clipping techniques that have been commonly used to stabilize gradient learning across multiple different network architectures. We will discuss a numerical technique to check gradients to assess the success of gradient learning. Finally, we will study methods to enhance learning convergence through adaptive learning algorithms.
1 Hour
2 Hours
1 Hour
1 Hour
1 Hour
1 Hour
2 Hours
OBJECTIVES
Upon completion of this course, participants will be able to:
WHO SHOULD ATTEND?
Dr Eric Ho Tatt Wei (UTP)
Dr Eric Ho Tatt Wei received his MS and PhD degrees in Electrical Engineering from Stanford University in Silicon Valley, USA specializing in computer hardware and VLSI systems, As part of his PhD research, he developed real-time systems for fruit flies for biological research to conduct automated inspection and guide robotic manipulation. He is currently pursuing applications of deep neural network technology to network analysis on MRI brain images.
*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)
*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)
Centre for Advanced & Professional Education (CAPE)
Level 16, Menara 2,
Menara Kembar Bank Rakyat,
50470, Jalan Travers,
Kuala Lumpur.
+605 - 368 7558 /
+605 - 368 8485
cape@utp.edu.my