Exploring into the Advancements of Deep Learning Technology: A Comprehensive Review
Nomaan Rizvi, Sahil Shende · 2024
The ascent of artificial intelligence has been intricately linked with the evolution of neural networks, a transformative technology that has redefined countless industries. From their humble origins as basic models to their contemporary manifestation as complex deep learning architectures, neural networks, including Artificial Neural Networks (ANN), Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), Generative Adversarial Networks (GAN), and the groundbreaking Transformers, have left an indelible mark on the AI landscape. This comprehensive review article offers a thorough exploration of neural networks, tracing their historical development, dissecting their fundamental components, unveiling the intricacies of training procedures, and highlighting the wide-ranging practical applications across diverse domains. By diving into the nuances of each neural network type, readers will develop a profound appreciation for how these innovations collectively shape the contemporary AI landscape, making this paper an essential resource for researchers, practitioners, and enthusiasts seeking to navigate the ever-evolving world of neural networks and their pivotal role in the field of artificial intelligence.