Advances of deep learning and related applications
Usha Rawat, Chandra Shekhar Rai · 2024
The way we approach complicated tasks in deep learning, a subset of machine learning, has transformed artificial intelligence which has shown exceptional development and innovation in recent years. In-depth coverage of cutting-edge methods, structures, and applications is provided in this chapter on the most recent developments in deep learning. This chapter focuses on advanced neural network architecture. We provide a detailed analysis of Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Transformer models. We provide a thorough analysis of these architectures’ principles and applications as we examine how they have changed the field of computer vision, NLP, and generative tasks. It gives scholars, professionals, and enthusiasts in the field of artificial intelligence a thorough and current resource. By enabling readers to effectively use neural networks for tackling some of the most difficult problems in the modern world, it acts as a guiding beacon across the constantly changing deep learning environment.