👉 Convolutional Neural Network (CNN) engineering involves designing and optimizing the architecture of CNNs, which are a class of deep learning models particularly effective for processing grid-like data such as images. The process begins with understanding the core components of CNNs, including convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply filters to the input data to extract features like edges and textures, while pooling layers reduce dimensionality and computational complexity. The engineering process focuses on optimizing these layers' configurations, such as filter sizes, number of filters, and stride values, to enhance the model's ability to learn and generalize from data. Additionally, techniques like data augmentation, regularization (e.g., dropout), and advanced architectures (e.g., residual connections, attention mechanisms) are employed to improve performance, robustness, and efficiency. The goal is to create a CNN that can effectively capture spatial hierarchies in images, leading to superior performance in tasks like image classification, object detection, and segmentation.
