Image Data Processing in CNNs ๐ผ๏ธ - Network Layers Explained ๐ - Topic 262 #ai #ml
Key Takeaways
This video covers the technical differences between convolutional filters and fully connected layers in Convolutional Neural Networks (CNNs), specifically how image data is processed and passed through these layers. It explains the concept of sparse connections in convolutional layers versus dense connections in fully connected layers.
Full Transcript
now we're going to take some time to break down the technical differences for what exactly is happening as the image data is passed to these convolutional filters versus what would happen otherwise if it were being passed to a fully connected layer when image data is passed to a network that's set up like this we first flatten the input into a one-dimensional tensor and each element in this tensor is a single Pixel value from the input image and then each pixel value within this flattened input is connected by a weight to every single node in the first hidden layer and we raised some caveats with this approach if you recall so then we started hypothesizing about an alternative way remember with this example we talked about passing groups of pixels from the input to given nodes in the first hidden layer rather than passing every single input value to every single node in the hidden layer so for example we have this group here in the top left is passed and processed only by this node in the hidden layer and then so on for the next two the key Point here is that because we are talking about passing groups of pixels together that are close to each other in the input space that these groups of pixels as input are only sparsely connected to the first HD layer rather than densely connected with the alternative approach where this first HD layer is a fully connected layer
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