First off, let’s start by defining what deep learning is: It’s basically when you take a bunch of data and throw it at a computer until it learns how to do something on its own. But don’t worry if that sounds too complicated we’re going to break it down for you in simple terms.

So, let’s say you have a dataset with pictures of cats and dogs. You want the computer to be able to identify which ones are cats and which ones are dogs. To do this, you feed the data into a neural network essentially a fancy algorithm that can learn from examples. The neural network will then try to figure out what features make a cat different from a dog (like fur color or ear shape), and use those features to classify new images as either cats or dogs.

Well, it’s not really magic it’s just math! And if you don’t believe us, we have some equations to prove it.

Here’s an example of what the neural network might look like:

y = Wx + b

Where y is the output (in this case, whether the image is a cat or a dog), x is the input (the pixel values in the image), W is the weight matrix that tells us how important each feature is, and b is the bias term. The neural network will adjust these weights and biases over time as it learns from more data.

Now, you might be wondering why do we call this “deep” learning? Well, because there are multiple layers of neurons in the neural network that allow it to process information at different levels of abstraction (hence the term “depth”). This is what allows it to learn complex patterns and relationships between data points.

We hope this helped clarify some of the more confusing concepts in AI, but if not, don’t worry just keep feeding your neural network more data until it figures it out on its own.