An In-Depth Exploration of Deep Learning

EasyChair Preprint 15495

Abstract

Deep learning, a subset of machine learning, has transformed the landscape of artificial intelligence (AI) with its ability to learn intricate patterns from data. This paper provides an in-depth examination of deep learning, encompassing its methodologies, applications, and recent advancements. We explore the historical progression of deep learning, compare it with traditional machine learning approaches, and analyze state-of-the-art architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Experimental results on benchmark datasets demonstrate the superiority of deep learning techniques in accuracy and scalability. Finally, we discuss potential challenges and future directions.

Keyphrases: Algorithms, CNN, RNN, deep learning

@booklet{EasyChair:15495,
  author    = {Mo Zhang and Behdad Jafari and Amin Bagheri and James Rajez and Mehmmet Amin},
  title     = {An In-Depth Exploration of Deep Learning},
  howpublished = {EasyChair Preprint 15495},
  year      = {EasyChair, 2024}}