This review article provides an overview of deep learning methods used in computer vision, focusing on Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Deep Boltzmann Machines (DBMs), and Stacked Denoising Autoencoders (SDAs). It discusses the history, structure, advantages, and limitations of these models, as well as their applications in various computer vision tasks such as object detection, face recognition, action recognition, and human pose estimation. The article also highlights future directions and challenges in designing deep learning schemes for computer vision problems. Key contributions include the introduction of CNNs, the development of DBNs and DBMs, and the advancements in SDAs. Despite their success, these models face challenges such as computational costs and the need for labeled data. The article concludes by emphasizing the importance of further research to address these challenges and improve the theoretical understanding of deep learning models.This review article provides an overview of deep learning methods used in computer vision, focusing on Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Deep Boltzmann Machines (DBMs), and Stacked Denoising Autoencoders (SDAs). It discusses the history, structure, advantages, and limitations of these models, as well as their applications in various computer vision tasks such as object detection, face recognition, action recognition, and human pose estimation. The article also highlights future directions and challenges in designing deep learning schemes for computer vision problems. Key contributions include the introduction of CNNs, the development of DBNs and DBMs, and the advancements in SDAs. Despite their success, these models face challenges such as computational costs and the need for labeled data. The article concludes by emphasizing the importance of further research to address these challenges and improve the theoretical understanding of deep learning models.