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Showing posts with the label models

Navigating the Diverse Landscape of Deep Learning Models: An Overview

Deep learning models come in various architectures and configurations, each tailored to address specific tasks and challenges across different domains. In this section, we'll explore the spectrum of deep learning models, highlighting their types, characteristics, and illustrative examples. 1. Convolutional Neural Networks (CNNs): Characteristics : CNNs excel in tasks involving image recognition, object detection, and computer vision. They leverage convolutional layers to extract spatial features hierarchically from input images, enabling robust and efficient pattern recognition. Examples : AlexNet, VGGNet, ResNet, and MobileNet are popular CNN architectures used for tasks such as image classification, object detection, and semantic segmentation. 2. Recurrent Neural Networks (RNNs): Characteristics : RNNs are well-suited for sequential data processing tasks, including natural language processing, time series analysis, and speech recognition. They maintain internal state (memory) to