It is well known that convolutional neural networks (CNNs or ConvNets) have been the source of many major breakthroughs in the field of Deep learning in the last few years, but they are rather unintuitive to reason about for most people. I’ve always wanted to break down the parts of a ConvNet and see what an image looks like after each stage, and in this post I do just that!

CNNs at a high level

First off, what are ConvNets good at? ConvNets are used primarily to look for patterns in an image. You did that by convoluting over an image and looking for patterns. In the first few layers of CNNs the network can identify lines and corners, but we can then pass these patterns down through our neural net and start recognizing more complex features as we get deeper. …


Erik Reppel

I like building stuff

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