All You Need to Know About Deep Neural Networks for Decision Makers and Technical Support

LEVEL : BEGINNER                HRDF : CLAIMABLE

TRAINER : DR ERIC HO TATT WEI

WHEN  


3 - 5 FEBRUARY 2021

WHERE 


MS TEAMS

TIME


9.00AM - 1.00PM

RM 650 FOR PROFESSIONAL

10% Discount for Early Bird / Group / Students

COURSE SUMMARY

INTRODUCTION

The course prepares participants to understand the key principles and trends in deep neural network artificial intelligence technology from an intuitive and approachable perspective. The course material will guide participants in practical ways to reason and interpret deep neural network AI performance and the challenges surrounding intellectual property, security and technological continuity. The course also provides insights that can assist participants to develop processes, infrastructure and best practices to support deep neural network technology development as well as to develop business models and strategies for deep neural network AI-enabled products.  

This course addresses the critical need in Malaysian industries to develop a cohort of competent technical professionals for marketing and support roles as a competitive advantage to win-over new customers in the global market for AI-enabled products. Such customer-facing roles are critical for business success but the technical gaps in their training are often overlooked especially in high-technology areas like AI due to overemphasis on technical training for the product development/ R&D team. The goal is to develop customer-serving technical professionals that can converse intelligently about the features, performance and prospects of deep neural network AI and respond impressively to customer demands for technical clarification or requests for features.  

COURSE CONTENT
1) Introduction of Deep Neural Networks.
2) Advantages of Deep neural Network over conventional Machine Vision algorithms.

3) The development trend of deep neural network AI technology.

4) Deep neural network analogy to democracy of experts.
  • Architecture as the hierarchy and relationship between voters and the flow of information.  
  • Inference as running an election.
  • Training as determining who is influential.
5) Dimensions of deep neural network performance.
  • Accuracy & robustness .
  • Speed .
  • Size/storage.
  • Handling outliers. 
6) Data strategy & life cycle management for deep neural networks.
  • Quality & diversity. 
  • Annotation and labeling.  
  • Synchronizing information from diverse sources.  
  • Data storage strategy.  
  • Data life cycle management.  
  • Life cycle costing for data.
  • Putting data to work continuously.  
7) Interpreting performance of a deep neural network.
  • Best case and worst case outcomes. 
  • Bias – Variance of the network.  
  • Confidence level & data diversity. 
  • Correlation vs causation.  
8) Extrapolating performance of deep neural networks.
  • Routine vs novel data. 
  • Making customer guarantees. 
9) Speed performance of deep neural networks.
  • Software and hardware contributions.  
  • Open source platforms vs custom code.  
  • Inference performance vs training performance. 
  • Energy-delay tradeoff.  
10) Safe deployment of deep neural networks.
  • Anomaly detection and worst-case outcomes.  
  • Trust, Correctness, Fairness, Accessibility.  
  • Potential legal liability.  
  • Mixed algorithms. 
11) Deep neural networks and data security.
  • Data vulnerabilities. 
  • DNN vulnerabilities.  
  • Adversarial deep learning.  
12) Intellectual property and deep neural networks. 
  • Novelty & sources of intellectual property. 
  • Reverse engineering. 
  • Open source vs trade secret.
13) The hidden energy cost of AI solutions.

14) Preparing for the future – upgrading deep neural networks.
  • What is an upgrade? 
  • New sales & product business models.  
  • Enriched data sources and new information streams.  
  • Backward compatibility challenges. 
15) Infrastructure for the future.
  • Topology, weights, neurons.  
  • Memory/storage requirements.
  • Reusable compute as a mechanism. 
  • IP and security challenges. 
16) Future trends of deep neural network AI.
  • Continuous online learning.  
  • Distributed learning.  
  • Explainable AI.  


OBJECTIVES

Upon completion of this course, participants will be able to:

  • To understand, identify and articulate key technical features and issues related to applications of deep neural network artificial intelligence technology in products to customers and to internal R&D team. 
  • To recognize and explain the importance of best practices in deep neural network artificial intelligence technology and infrastructure in various product and customer settings. 

  • To make informed decisions related to sales and support of products with deep neural network artificial intelligence technology.


WHO SHOULD ATTEND?

  • Managers and Executives.
  • Technical Support and Customer Support Teams.





OUR TRAINERS



1. 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.

COUNTDOWN

DaysHoursMinutesSeconds

REGISTRATION FEES

      Professional       

myr650

*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)

Early bird/ group/ student

myr585

*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)

OUR LOCATION

Centre for Advanced & Professional Education (CAPE)

 Level 16, Menara 2, Menara Kembar Bank Rakyat, 50470, Jalan Travers, Kuala Lumpur.

CALL US

+605 - 368 7558

DROP US AN EMAIL

cape@utp.edu.my