Deep Learning and Beyond: A Comprehensive Study of Neural Networks

Authors: Dr. Sandip Majumdar

Country: India

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Abstract: Neural networks have fundamentally transformed artificial intelligence, leading to groundbreaking progress across multiple domains, including computer vision, natural language processing, and autonomous systems. This paper offers a comprehensive examination of neural networks, beginning with their historical evolution and foundational principles. We explore various architectures such as feedforward, convolutional, and recurrent neural networks, detailing their unique structures and functions. The paper also delves into training methodologies, including backpropagation, gradient descent, and advanced optimization techniques. Additionally, we highlight the diverse applications of neural networks, ranging from image recognition and speech synthesis to autonomous vehicle navigation and medical diagnosis. Current challenges, such as model interpretability, computational efficiency, and ethical considerations, are thoroughly discussed. Finally, we outline future research directions, emphasizing the potential for neural networks to drive further innovations in AI and beyond.

Keywords: Neural networks, Artificial intelligence, Computer vision, Natural language processing, Autonomous systems, Feedforward networks, Convolutional networks, Recurrent networks, Backpropagation, Gradient descent, Optimization techniques, Image recognition, Speech synthesis, Autonomous vehicle navigation, Medical diagnosis, Model interpretability, Computational efficiency, Ethical considerations in AI, AI research directions, AI innovations

Paper Id: 230849

Published On: 2021-01-03

Published In: Volume 9, Issue 1, January-February 2021

Cite This: Deep Learning and Beyond: A Comprehensive Study of Neural Networks - Dr. Sandip Majumdar - IJIRMPS Volume 9, Issue 1, January-February 2021.

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