Unveiling the Depths of Neural Networks: Exploring Architectures, Training Techniques, and Practical Implementations

EasyChair Preprint no. 12053

Abstract

Neural networks, inspired by biological neural systems, have revolutionized the field of artificial intelligence, driving advancements in various domains from image and speech recognition to medical diagnostics and autonomous vehicles. This paper offers an extensive exploration into the intricate world of neural networks, delving deep into their architectures, training methodologies, and real-world applications. Beginning with a foundational overview of neural network structures, the paper progresses to discuss state-of-the-art training techniques, emphasizing the significance of backpropagation, optimization algorithms, and regularization methods. Furthermore, practical implementations across diverse sectors are highlighted, showcasing the transformative potential of neural networks in addressing complex challenges. Through this comprehensive analysis, the paper aims to provide readers with a holistic understanding of neural networks, elucidating their underlying principles and showcasing their profound impact on modern computational paradigms.

Keyphrases: architectures, Artificial Intelligence, Backpropagation, neural networks, optimization algorithms, practical implementations, regularization, Training Techniques

@Booklet{EasyChair:12053,
  author = {Jonny Bairstow},
  title = {Unveiling the Depths of Neural Networks: Exploring Architectures, Training Techniques, and Practical Implementations},
  howpublished = {EasyChair Preprint no. 12053},

  year = {EasyChair, 2024}}