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Thousands of conditions! 🤯

Perfect. We’ve found a set of weights that work well with our few conditions. But what if our social media model had not just three factors but hundreds or maybe thousands?

Trying to guess the right combination of weights in this situation is like finding a needle in a haystack – actually, it’s more like finding a specific grain of sand on a beach!

The Challenge with a huge number of conditions​

Complexity Multiplies: With just a few conditions, it’s like juggling a few balls. But with thousands, it’s like juggling an entire circus worth of objects while blindfolded.

Guessing Becomes Impractical: If guessing the right combination for three conditions is like cracking a simple three-digit lock, then doing the same for thousands of conditions is like guessing the combination of a vault with a thousand dials. It’s not just difficult; it’s practically impossible!

Computational Burden: This isn’t just a mental challenge; it’s a computational one. The more conditions you have, the more possible combinations of weights there are, exponentially increasing the amount of time and resources needed to test each possibility.

guess wifi password

Imagine trying to guess the WiFi password for a coffee shop that has a password composed of 100 random characters.

You sit down and start guessing: “coffee123”, “bestcoffeeintown”, “ilovecoffee”… As you add more characters, the likelihood of you hitting the right password by random guesses becomes laughably impossible.

It’s the same with our weights - as we add more conditions, simply guessing becomes almost impossible.

Better Approach​

Luckily, machine learning doesn't rely on guesswork.

Instead, it uses awesome algorithms to find those 'hidden' weights and patterns all by itself.

In the next chapters, we'll see how these algorithms work, one step at a time, breaking them down to ensure we understand exactly what's going on.

By the end, you will be able to use them to build your own super-powerful machine-learning models!

I can't wait to see what you create. See you in the next chapter!