I am Dev Vyas, a Pandit Deendayal Energy University student in my B. Tech 3rd Year.
I have a strong background in Machine Learning, Deep Learning, and Reinforcement Learning, having studied these subjects extensively and honed my skills through various research projects and internships. I have developed a thorough understanding of the underlying algorithms and mathematical concepts, as well as hands-on experience with programming languages such as Python, C++, Java, JavaScript, and Matlab. These experiences have equipped me with the knowledge and skills required to pursue a master's degree in this field.
My research experience in Vehicle-to-Vehicle (V2V) communication has provided me with a unique perspective on the potential of AI and Machine Learning to improve the transportation industry. I created an environment for a highway scenario in Python and implemented a Deep Q Network (DQN), which demonstrated my ability to translate theoretical concepts into practical applications. This experience has further fueled my passion for AI and Machine Learning and my desire to make a positive impact in this field.
My ultimate goal is to use my education and experience to develop new products that will make the world a better place and address some of the pressing issues facing society today. I am particularly interested in applying AI and Machine Learning to areas such as healthcare, transportation, and education. I believe that these areas have the potential to greatly benefit from AI and Machine Learning and that my research in these areas could have a significant impact on society.
This project involved reinforcement learning for Vehicle-to-Vehicle (V2V) communication. In this project, I was responsible for developing an environment for a highway scenario as outlined by the 3rd Generation Partnership Project (3GPP). I successfully integrated this environment with a Q-learning model built using Tensorflow, with the help of Numpy for computation and manipulation of arrays. This project utilized Numpy extensively, both for the environment and the Q network.