Guide to Convolutional Neural Networks

David Landup
David Landup

What are Convolutional Neural Networks?

We've had a short introduction to CNNs as a concept in the previous lesson, so let's dive into them in a bit more detail here. Convolutional Neural Networks (CNNs) are, as mentioned earlier, a type of Deep Artificial Neural Networks (ANNs). The name arises from their use of convolutional layers which perform feature extraction - more on them a bit later.

Note: Convolutional layers, and the convolution operation are at the core of CNNs and it's worth taking out a bit of time to really understand what's going on there.

Again, like with most other learning networks, CNNs aim to mimic the human brain (currently best-known learning algorithm) - and in this case, the visual perception that humans have. With the expansion of the neocortex, humans (and other mammals, but to a lesser degree) were able to start noticing intricate abstract patterns in the world around them. This higher-level abstraction is what, down the line, allowed us to make out symbols and give them meaning - giving birth to art, culture and language.

These things are hard to mimic, in large part because they're hard to explain. If you ask a musician or artist to explain their notes or strokes with a brush - more often than not, you'll receive an answer along the lines of:

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