Types of Neural Networks

Quick reminder. Neural networks are how AI thinks. But — not every neural network looks the same. Different problems need different shapes. Like a hammer for nails and a saw for wood. Today we meet three. They power almost every AI you know.

Here's the cheat sheet. CNNs see — they're for images. RNNs handle sequences — text, speech, music. Transformers understand context — they're behind ChatGPT. Let's pop the hood on each.

Number one — CNN. Stands for Convolutional Neural Network. Big word for a simple idea. CNNs look at images by examining tiny patches at a time. Edges. Shapes. Colors. Then they combine the patches to figure out the whole picture. That's how phones recognize your face.

Where do you see CNNs? Everywhere there's a camera and a question. Face unlock. Searching photos by content. Doctors finding tumors. Self-driving cars spotting pedestrians. All CNNs.

Number two — RNN. Recurrent Neural Network. RNNs are different. They process info one piece at a time AND keep a kind of memory of what came before. Like reading a sentence — you don't get the meaning from one word, you get it from the whole flow.

RNNs were big for speech, translation, and predicting next words. They've mostly been replaced by transformers now — but the idea — process in order, keep memory — that's still the spirit behind a lot of AI.

Number three — the Transformer. The big one. Transformers don't go word by word. They look at every part of the input AT ONCE — and figure out which parts matter to which. Like reading a whole paragraph and instantly knowing which words go together. That's called attention. [pause:400] And it changed everything.

Where do you see transformers? Everywhere. ChatGPT — transformer. Claude — transformer. Image generators like Midjourney — transformer based. Coding assistants — transformers. Pretty much every AI you use day-to-day in 2026.

So that's the trio. CNNs see — built for pixels. RNNs read in order — built for sequences. Transformers connect everything — built for context. Same brain idea, three shapes for three problem types.

Your turn. I'll give you a task — you pick the right neural net for it. CNN, RNN, or Transformer. Six rounds. Some are sneaky.

Lock it in. CNN — vision. RNN — sequences. Transformer — context. Real AI projects often combine them. A self-driving car uses CNNs to see, plus transformers for planning. The best engineers know which tool fits which job.

Cool — now you know the shapes. Next big question — once an AI is trained, what happens? Does it stay frozen forever? Or does it keep learning? Let's look at the AI lifecycle.