The AI Glossary: Deep learning

Welcome to another episode of ‘Decoding AI’!

Today, we’re taking on the mammoth task of unravelling ‘Deep Learning.’ Now, don’t worry; you won’t need a PhD for this. We’ll keep it crispier than your favourite pack of crisps. So, let’s dive right in, shall we?

What on earth is Deep Learning?

Deep learning, my friends, is that buzzword you’ve probably heard about a million times. It’s a subset of machine learning – think of it as the brainy kid in the family of artificial intelligence. Basically, it uses algorithms called neural networks, inspired by our own brain’s structure, allowing computers to learn and make sense of large datasets, such as images, text, or audio.

A Slice of Context

Imagine you’re at a cocktail party with music blaring around. You’re trying to understand a joke your friend made about marketers. Amongst all the chaos, you filter out your friend’s voice, understand the joke, laugh hysterically and everyone thinks you’re life of the party. That’s you doing ‘deep learning.’ Fascinating, isn’t it?

Deep Learning: A ‘Real-World’ Analogy

Picture deep learning as your favourite cooking show. Stick with me here. Like how the chef tosses a bunch of ingredients (big data) into a pot (computer), stirs it up (algorithm), and voilà – out comes a gourmet dish ready to eat (meaningful output)!

So, there you have it, folks! The heady world of deep learning, simplified for anyone who’s ever followed a cooking show or been the life of a party. Until next time, keep decoding!

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