Saransh Rastogi

• This master’s thesis aims to explore the potential of utilizing deep learning methodologies, including convolutional neural networks (CNNs) as well as feed forward networks (FNNs) within a learning framework to address both classification and regression problems simultaneously. The primary objective is to investigate the feasibility of developing a unified neural network architecture capable of handling diverse problem types. This research will not only assess the model’s predictive performance in terms of accuracy and efficiency but also delve into the underlying mechanisms contributing to its effectiveness.