What is it about?
This paper aims to comprehensively overview the state-of-the-art artificial neural network (ANN) generation and highlight key challenges and opportunities in machine learning applications. It provides a critical analysis of current neural network model design methodologies, focusing on the strengths and weaknesses of different approaches. Also, it explores the use of different deep neural networks (DNN) in image recognition, natural language processing, and time series analysis. In addition, the text explores the advantages of selecting optimal values for various components of an Artificial Neural Network (ANN).
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Why is it important?
The paper identifies some common pitfalls and limitations of existing design methodologies, such as overfitting, lack of interpretability, and computational complexity. Finally, it proposes some directions for future research, such as developing more efficient and interpretable neural network architectures, improving the scalability of training algorithms, and exploring the potential of new paradigms, such as Spiking Neural Networks, quantum neural networks, and neuromorphic computing.
Perspectives
It is my pleasure to publish this paper and contact the audience. Neural Networks have become more common in different areas in the last few years due to their ability to learn intricate patterns and provide precise predictions. Nonetheless, creating an efficient neural network model is a difficult task that demands careful consideration of multiple factors, such as architecture, optimization methods, and regularization techniques.
Prof Dr. Jabar H. Yousif
Sohar University
Read the Original
This page is a summary of: Evolutionary Perspectives on Neural Network Generations: A Critical Examination of Models and Design Strategies, Current Computer Science, October 2024, Bentham Science Publishers,
DOI: 10.2174/0129503779282967240315040931.
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