Seminar on "Deep Learning"

06/03/2023 - 3:47pm

The Department of Electronic Engineering organized a seminar on "Deep Learning" on 10th November 2022. The seminar aimed at providing participants with an in-depth understanding of deep learning techniques and their applications in the field of electronics engineering.

The seminar was opened by the Chairperson of the Department of Electronic Engineering, Professor Dr. Arbab Nighat, who gave a warm welcome to the resource person, Engr. Arsha Kumari, lecturer, ES department, and the participants. Professor Dr. Arbab Nighat introduced the topic of the seminar and its importance in the electronic engineering field. The resource person, Engr. Arsha provided a comprehensive overview of deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). She also discussed the applications of deep learning in areas such as computer vision, natural language processing, and speech recognition.

In addition to theoretical explanations, Engr. Arsha also conducted interactive sessions and practical demonstrations to provide the participants with hands-on experience in implementing deep learning techniques.

In the concluding remarks, the Chairperson of the Department of Electronic Engineering, Professor Dr. Arbab Nighat thanked Engr. Arsha and the participants for attending the seminar. She also appreciated the efforts of departmental Event Management Committee for organizing such seminar.

In conclusion, the "Deep Learning" seminar was a resounding success and provided valuable insights into the field of deep learning and its applications in electronics engineering. The Event Management Committee of the Department of Electronic Engineering and the IEEE IMS Society Chapter should be commended for their efforts in organizing such a valuable seminar. The participants left the seminar feeling motivated and inspired to apply what they had learned in their future careers.

Pictures of Seminar on Deep Learning