Deep Learning Techniques for Image Recognition and Classification

Abstract

Convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and hybrid models are the main topics of this research study on deep learning approaches for image recognition. CNNs are very accurate and efficient when it comes to static images; RNNs are good at sequential data jobs; GANs work well for generative applications; and Hybrid Models work better when it comes to complex tasks. Despite their complexity, hybrid models have potential, as demonstrated by a comparative analysis. To advance image recognition technology, future studies should improve these models' efficacy, stability, robustness, and real-time capabilities.