Deep Learning: Mathematical Foundations, Applications, and Experimental Insights

EasyChair Preprint 15723

Abstract

Deep learning, a subset of artificial intelligence, has emerged as one of the most influential and transformative technologies of the modern era. Leveraging artificial neural networks, deep learning enables machines to identify patterns, make decisions, and perform tasks that often surpass human capabilities in domains like image recognition, speech processing, and natural language understanding.

This paper provides an in-depth exploration of the mathematical foundations underlying deep learning, focusing on neural network architectures, activation functions, optimization algorithms, and regularization methods. A comprehensive review of standard models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and emerging architectures like Vision Transformers (ViTs), is presented to highlight their strengths and limitations.

In addition to theoretical insights, this study evaluates the performance of these models on benchmark datasets, such as CIFAR-10, and presents experimental results that demonstrate their efficiency and accuracy. The results are compared across models in terms of training time, accuracy, and computational resources, providing a holistic understanding of their real-world applicability.

The paper also addresses the challenges facing deep learning, including data dependency, interpretability, and energy consumption, and discusses potential future advancements, such as more efficient algorithms, lightweight architectures, and explainable AI systems. By synthesizing theoretical and experimental findings, this work aims to offer a clear and structured framework for researchers and practitioners in advancing deep learning applications.

Keyphrases: AI, Application, deep learning, neural network

@booklet{EasyChair:15723,
  author    = {H Chung and Ali Rashed and Rene Gonzalens and Isabel Cheng and James Kung},
  title     = {Deep Learning: Mathematical Foundations, Applications, and Experimental Insights},
  howpublished = {EasyChair Preprint 15723},
  year      = {EasyChair, 2025}}