How Exactly Does AI Learn?

Artificial Intelligence is everywhere today, but many people don’t understand how machines learn. The truth is, it’s not magic – it's all about something called neural networks and deep learning.

In this newsletter, we’ll break it down in simple terms.

What Are Neural Networks?

Think of a neural network like a human brain (but way simpler!). It’s made up of layers of artificial neurons that work together to solve problems. You’ve got:

• An input layer that takes in data (like an image or some text).
• Hidden layers that process the data.
• An output layer that gives the final result (for example, a prediction).

Each layer passes information to the next, refining it step by step.

How Do Neural Networks Learn?

Neural networks learn through something called training. We give them lots of data, and they start recognizing patterns in that data. To improve, the network adjusts its internal settings – these are called weights and biases.

Here’s the basic process:

1. Forward Pass: Data moves through the network, layer by layer, until it reaches the output.
2. Calculate Error: At first, the output is usually wrong. The network measures how far off it is from the correct answer using a loss function.
3. Backpropagation: The network works backward, adjusting its weights and biases to improve. Over time, with enough data and training, the network gets better and better at making accurate predictions.

The Role of Activation Functions

Not every signal in a neural network passes through. Neurons need to “decide” whether to activate and send the signal forward. This is done using activation functions – these are mathematical functions that control how much signal moves through the network.

Popular activation functions include Sigmoid and ReLU, which help make sure that neurons are firing correctly based on the input they receive.

Applications of Neural Networks

Neural networks have endless applications! They’re used in:

• Image recognition (like facial recognition in your phone)
• Language translation (turning speech or text from one language to another)
• Predicting outcomes in industries like healthcare or finance.

The more data you feed them, the better they get!

Conclusion

In a nutshell, neural networks learn by adjusting themselves based on the data they receive. They mimic how the brain works, but with a focus on specific tasks like identifying patterns in images or predicting outcomes. It’s fascinating how something that sounds complex can be simplified once you break it down!

For a more detailed explanation with visuals, check out my latest YouTube video: [How Exactly Does AI Learn? Breaking Down Neural Networks and Deep Learning]