Greater Noida:Department of Electronics and Communication Engineering, I.T.S. Engineering College organized webinar on most demanding topic “Classification of objects using Deep Networks”. The webinar was conducted by Dr. Dinesh K. Vishwakarma Associate Professor, Department of Information Technology, Delhi Technological University. I.T.S.E.C. Executive Director- Dr. Vikas Singh, Director- Prof. Sanjay Yadav and Dean (Academics)- Prof. Gagan Deep Arora presence graced the program. The session was moderated by HOD ECE- Prof. Monika Jain.

Vice Chairman of I.T.S Engineering College, Mr. Sohil Chaddha has appreciated that learning through this kind of niche technologies Expert webinars during the COVID-19 lockdown period will enhance students knowledge and exposure. The entire session was very interactive, and the students have been answered with various queries related to deep networks. Dr. Dinesh K. Vishwakarma has given various examples related to the ANN & Deep Learning, shown some practical concept about deep learning like object recognition. Object classification is a general term to describe a collection of related computer vision tasks that involve classifying objects in digital photographs. The Image classification consists in predicting the class of one object in an image. Object detection combines these two tasks and localizes and classifies one or more objects in an image. The traditional methods of detection involved using a block-wise orientation histogram (SIFT or HOG) feature which could not achieve high accuracy in standard datasets such as PASCAL VOC. These methods encode very low-level characteristics of the objects and therefore are not able to distinguish well among the different labels. Deep learning (Convolutional Neural Networks) based methods have become state-of-the-art in object classification in images. They construct a representation hierarchy with increasing order of abstraction from lower to higher levels of neural network. In the present day, Deep Networks are very popular for objects classification due to high efficiency.The participants have gained extensive details of subject knowledge along with practical knowledge. Practical examples enriched their concept on Deep Learning, Object recognition and ANN. Program attendance was more than 60 participants (including faculty and students from various colleges