Fundamentals of Training AI Models for Computer Vision and Video Analytics Applications – Part 2

AI has become an important component of computer vision and video analytics applications. But creating AI-based solutions is a challenging process. To build a successful product, it is essential that training a deep neural network (DNN) results in a model which is highly accurate, robust to false positives and has high throughput. While approaching a new computer vision and video analytics task, an AI engineer needs to make a number of design decisions. How do we formulate a deep learning problem? How much data is enough? How complex shall the model be for a particular task? How to set training parameters? In this talk, we will share some best practices an AI developer can follow to answer these and other important questions when developing a new AI system in order to get meaningful results faster.