The Department of Computer Science and Engineering (CSE) at SiliconTech, the engineering institute of Silicon University, organized a technical talk on ‘Introduction to Deep Learning’ on 31 January 2024.
The talk was delivered by Mr. Shanti Swaroop, a technical architect at Tata Consultancy Services Limited (TCS). Prior to this, he worked with Morgan Stanley, AT& T, Capgemini, Accenture, and Jataayu Software. Having worked in multiple programming languages for over sixteen years, he has expertise in C, C++, Java, Scala, Python, SQL, and frameworks such as Hadoop, Spark, Big Data, and microservice frameworks used in telecom, retail, recommendation engines, and healthcare. His interests lie in data science, machine learning, and deep learning.
The objective of the talk was to enhance the understanding of Least Mean Squares(LMS) and gradient descent algorithms and learn about building them from scratch in Python for various signal processing and optimization applications.
In his talk, the speaker explained that gradient descent and least mean squares (LMS) algorithms are fundamental optimization techniques widely used in machine learning and signal processing. He discussed using gradient descent to minimize the cost function of a model. He also emphasized the use of the Least Mean Squares (LMS) algorithm as a popular adaptive filtering algorithm for signal processing tasks such as noise cancellation and system identification.
A total of ninety students and faculty members attended the talk to enhance their understanding of deep learning.
