A Deep Dive into Convolutional Neural Networks. What goes inside a CNN? Why CNN is so powerful?

Convolutional Neural Networks (CNNs) are a class of deep neural networks specifically designed to process and analyze visual data. They are widely used in computer vision tasks such as image recognition, object detection, and segmentation due to their ability to automatically and adaptively learn spatial hierarchies of features from input images.
Architecture
A typical CNN architecture consists of:
Input Layer:
Accepts the raw pixel values of the input image.
Convolutional Layers:
Multiple layers of convolutional filters are applied to learn various features.
Pooling Layers:
Reduce the spatial dimensions and compute summaries of features.
Fully Connected Layers:
Serve as classifiers on top of the extracted features.
Output Layer:
Provides the final classification or regression output.

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