Deep Learning: Practical approach

Bhavatarini N · Syed Muzamil Basha · Syed Thouheed Ahmed

Sep 2022 · MileStone Research Publications

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In a very short time, deep learning has become a widely useful technique, solving and automating problems in computer vision, robotics, healthcare, physics, biology, and beyond. One of the delightful things about deep learning is its relative simplicity. Powerful deep learning software has been built to make getting started fast and easy. In a few weeks, you can understand the basics and get comfortable with the techniques.

This opens up a world of creativity. You start applying it to problems that have data at hand, and you feel wonderful seeing a machine solving problems for you. However, you slowly feel yourself getting closer to a giant barrier. You built a deep learning model, but it doesn’t work as well as you had hoped. This is when you enter the next stage, finding and reading state-of-the-art research on deep learning.

However, there’s a voluminous body of knowledge on deep learning, with three decades of theory, techniques, and tooling behind it. As you read through some of this research, you realize that humans can explain simple things in really complicated ways. Scientists use words and mathematical notation in these papers that appear foreign, and no textbook or blog post seems to cover the necessary background that you need in accessible ways. Engineers and programmers assume you know how GPUs work and have knowledge about obscure tools.

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Associated with school of computer science and engineering, REVA University.

Syed Muzamil Basha, received his B.E. in Computer Science and Information Technology, from Jawaharlal Nehru Technological University, Andhra Pradesh in 2008 and M.Tech. and Ph.D. degree in the School of Computer Science and Engineering from the VIT (Deemed-to-be University), Vellore, India. He is having 11+ years of teaching cum research experience. He is Currently associated with School of Computer Science and Engineering, REVA University, Bangalore, Karnataka. India. His current research interests include Big-Data Analytics, Block-Chain Management, Internet of Things. He has 30+ publications, which includes research papers - 20 (Scopus, SCI) and conferences (International)-7, Book Chapters-6, Letters-2. He is one of the editors for the textbooks - 7, Conducted Guest Lecture in various Engineering Colleges in India. He is associated to University of Hail, Saudi Arabia as Sponsored research consultant on designing and developing advance machine learning techniques in biomedical systems.

Syed Thouheed Ahmed, received his B.E. and M.Tech. in Computer Science and Engineering from Visveswaraya Technological University, Karnataka, India in 2013 and 2015 respectively and has a Ph.D. in Computer Science and Mathematics from BSA Crescent Institute of Science and Technology, (deemed-to-be University), Chennai, Tamil Nadu, India. He is currently associated with School of Computing and Information Technology, REVA University, Bengaluru, India. He has 70+ publications in various peer reviewed international and national journals and conferences. He is associated to University of Hail, Saudi Arabia as Sponsored research consultant on designing and developing advance machine learning techniques in biomedical systems.