This review article discusses the application of deep learning in computer vision, focusing on key models such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Deep Boltzmann Machines (DBMs), and Stacked Denoising Autoencoders (SdAs). It provides an overview of their history, structure, advantages, and limitations, followed by their applications in tasks like object detection, face recognition, action recognition, and human pose estimation. The article also highlights future directions and challenges in deep learning for computer vision. Deep learning has significantly improved performance in computer vision tasks by enabling models to learn complex features automatically. CNNs, which are particularly effective for image processing, have shown superior performance in tasks such as object detection and face recognition. DBNs and DBMs are probabilistic models that use Restricted Boltzmann Machines (RBMs) for unsupervised learning, while SdAs are used for feature learning and unsupervised pretraining. Each model has its strengths and weaknesses, with CNNs excelling in feature learning and invariance to transformations, but requiring labeled data. DBNs and DBMs can work unsupervised but are computationally intensive. SdAs offer real-time training but lack generative modeling capabilities. The article also discusses the use of deep learning in various applications, including object detection, face recognition, action and activity recognition, and human pose estimation. It highlights the importance of large datasets and the role of frameworks like TensorFlow and Theano in facilitating deep learning research. Challenges remain in terms of computational demands, model selection, and understanding the effectiveness of different architectures. The review concludes that while deep learning has made significant strides in computer vision, further research is needed to address theoretical and practical challenges.This review article discusses the application of deep learning in computer vision, focusing on key models such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Deep Boltzmann Machines (DBMs), and Stacked Denoising Autoencoders (SdAs). It provides an overview of their history, structure, advantages, and limitations, followed by their applications in tasks like object detection, face recognition, action recognition, and human pose estimation. The article also highlights future directions and challenges in deep learning for computer vision. Deep learning has significantly improved performance in computer vision tasks by enabling models to learn complex features automatically. CNNs, which are particularly effective for image processing, have shown superior performance in tasks such as object detection and face recognition. DBNs and DBMs are probabilistic models that use Restricted Boltzmann Machines (RBMs) for unsupervised learning, while SdAs are used for feature learning and unsupervised pretraining. Each model has its strengths and weaknesses, with CNNs excelling in feature learning and invariance to transformations, but requiring labeled data. DBNs and DBMs can work unsupervised but are computationally intensive. SdAs offer real-time training but lack generative modeling capabilities. The article also discusses the use of deep learning in various applications, including object detection, face recognition, action and activity recognition, and human pose estimation. It highlights the importance of large datasets and the role of frameworks like TensorFlow and Theano in facilitating deep learning research. Challenges remain in terms of computational demands, model selection, and understanding the effectiveness of different architectures. The review concludes that while deep learning has made significant strides in computer vision, further research is needed to address theoretical and practical challenges.