Advancements in Neural Network Generations

Abstract

Innovations in Neural Network Generation demonstrate the continual evolution, optimization, and development of artificial neural networks (ANNs) over periods. These improvements include a combination of methodologies, approaches, and technical breakthroughs aimed at increasing the efficiency and abilities of neural network models. Researchers and engineers have repeatedly attempted to push the boundaries of neural network performance, scalability, and applicability across multiple fields. These improvements usually involve changes to network designs, training algorithms, optimization methodologies, and hardware acceleration methods. Moreover, the neural network generations are closely related to key achievements in the machine learning (ML) research domain, such as the development of deep learning (DL) designs like convolutional neural network (CNN) or spiking neural network (SNN) and using both neural generations to introduce natural language processing and advances in computer vision applications. Thus, in the field of neural network study, researchers have categorized ANN models into generations based on their computational design and capabilities. Therefore, this research study explores the continual evolution and optimization of ANNs, highlighting advancements in methodologies and technical innovation. We discuss the different generations of ANN, based on computational design and capabilities, emphasizing their role in shaping achievements in ML research. The study underscores the significance of these generational milestones in enhancing the adaptability and efficacy of neural network models for computational tasks, such as image classification.