End to End Computer Vision paradigm with respect to advanced deep learning techniques.
Deep learning based approaches to solve image classification have become a core technology in AI, largely due to developments in computing powers and digital data. However image classification gained popularity beyond academic circle with the advent of visual object recognition challenge.
In this talk, we will walk through the journey of deep learning in the field of computer vision. The main focus will be on the most recent and advanced technique for image classification and object detection .We will walk through various classical architecture and in the journey will learn concepts like padding, max pooling .
To make the talk more interactive we will show live demo and code run of various use cases like car detection for autonomous driving.
Keywords: Object detection, Transfer Learning, Art transfer, Max-Pooling,Padding
Outline/Structure of the Tutorial
- What is CNN ?
- Classic Architecture
- Data Augmentation
- Transfer Learning
- Fine Tuning
- Case Study and applications
- Autonomous Driving Car Detection
- Overview of transfer learning
We will explain implementation of case study with jupyter notebook to get hands on experience.
By the end of this session, the audience will have a clear idea about how to tackle various problems of Computer Vision. The audience will get fundamentals of how various deep learning models work and the latest innovations that have taken place in Computer Vision.
Anyone who is interested in latest developments in deep learning and wants to understand how modern techniques have evolved in the field of computer vision.
Prerequisites for Attendees
Basic understanding of deep learning and how neural networks are trained. Beginner level knowledge about Python and Keras will be helpful in understanding the concepts more efficiently.