Discovering the Diversity of Neural Networks: An In-Depth Analysis of Architectures and Applications
Author(s):
Amandeep Kaur, Gagandeep Jagdev
Keywords:
CNN, FNN, LSTM, Neural Networks, RNN
Abstract
This research paper explores the evolution, principles, and applications of neural networks, a cornerstone technology in artificial intelligence and machine learning. Neural networks are computational models inspired by the human brain's neural connections, comprising interconnected nodes organized into layers. The paper delves into the fundamental concepts of neural networks, including Feed-Forward and Recurrent architectures, backpropagation for training, and various types of activation functions. It examines how neural networks learn from data through supervised, unsupervised, and reinforcement learning paradigms, emphasizing their ability to extract meaningful patterns and make accurate predictions from complex datasets. Furthermore, the research explores cutting-edge applications of neural networks across diverse domains, such as computer vision, natural language processing, speech recognition, and autonomous systems. Case studies and empirical results demonstrate the efficacy of neural networks in solving real-world challenges, ranging from medical diagnostics and financial forecasting to autonomous driving and personalized recommendations. Ethical considerations and challenges in neural network deployment, including issues of bias, interpretability, and scalability, are also discussed. The paper underscores the importance of responsible AI development practices and regulatory frameworks to address these concerns and ensure the ethical use of neural network technologies. In conclusion, this paper provides a comprehensive overview of neural networks, highlighting their transformative impact on technology and society. By synthesizing current research trends and future directions, it aims to guide researchers, practitioners, and policymakers in harnessing the full potential of neural networks while addressing critical challenges in their adoption and implementation.
Article Details
Unique Paper ID: 165720

Publication Volume & Issue: Volume 9, Issue 7

Page(s): 963 - 972
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