Imagine you're looking at a picture. Instead of seeing every single pixel at once, you might first focus on small parts – an edge, a corner, a texture. Then, you combine these small features to understand larger parts, and eventually, the whole object.
That's kind of how a CNN works.
Convolutional Layers: These are like our initial focus on small parts. They use "filters" that slide over the input image, looking for specific patterns (like those edges and corners). When a filter finds a pattern, it highlights it.
Pooling Layers: These help to simplify things. After finding patterns, we might not need the exact location of every single highlight. Pooling layers reduce the amount of information, making the network more efficient and robust to small shifts in the input. Think of it as summarizing the presence of a feature in a small region.
(Multiple Convolutional and Pooling Layers): We often stack these layers. The early layers learn basic features, and as we go deeper, the network learns more complex, abstract features by combining the simpler ones.
Fully Connected Layers: Finally, after extracting these features, we need to make a decision (like classifying the image). The fully connected layers take the high-level features learned by the convolutional and pooling parts and use them to output a final classification or prediction, just like a standard neural network.
So, in short, CNNs use convolution to find local patterns, pooling to simplify, and then fully connected layers to make a final decision based on those learned patterns.
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Imagine you're looking at a picture. Instead of seeing every single pixel at once, you might first focus on small parts like an edge, a corner, or a texture. Then, you combine these small features to understand larger parts, and eventually, the whole object. That's kind of how a Convolutional Neural Network works.
Convolutional layers are like our initial focus on small parts. They use filters that slide over the input image, looking for specific patterns like edges and corners. When a filter finds a pattern, it highlights it in the feature map, creating a representation of where that pattern appears in the image.
Pooling layers help to simplify things. After finding patterns, we might not need the exact location of every single highlight. Pooling layers reduce the amount of information, making the network more efficient and robust to small shifts in the input. Think of it as summarizing the presence of a feature in a small region.
We often stack these convolutional and pooling layers. The early layers learn basic features like edges and corners. As we go deeper into the network, it learns more complex and abstract features by combining the simpler ones from earlier layers. This hierarchical learning allows CNNs to understand increasingly sophisticated patterns.
In summary, CNNs use convolution to find local patterns, pooling to simplify, and then fully connected layers to make a final decision based on those learned patterns. This hierarchical approach mimics how humans process visual information, starting with simple features and building up to complex understanding.