17. What is an Artificial Neural Network (ANN)? (Easy Explanation) 🧠

An Artificial Neural Network (ANN) is a computer program inspired by the way our brains work. Just like our brain has neurons that process information, an ANN has artificial neurons (also called nodes) that take inputs, process them, and produce an output.

Think of it like this:

You see a picture of a cat 🐱 (input).
Your brain processes the image and recognizes features like whiskers, ears, and eyes (hidden layers).
You decide, "Yes, this is a cat!" (output).

Similarly, an ANN works by passing information through layers of artificial neurons:

Input Layer – Takes raw data (like an image or numbers).
Hidden Layers – Process data using mathematical operations (like recognizing patterns).
Output Layer – Gives the final prediction (e.g., "This is a cat!").

Each connection in the network has a weight, which tells the network how important a particular input is. During training, the ANN adjusts these weights to improve accuracy.

ANN Implementation in PyTorch 🏗️

Here’s a simple ANN class in PyTorch that can classify numbers (like digits in MNIST dataset):

How This Works? 🤔

First layer (fc1): Connects input to hidden layer.
Activation (ReLU): Adds non-linearity (helps in learning complex patterns).
Second layer (fc2): Connects hidden layer to output.
Forward pass (forward method): Defines how data moves through the network.

This is a basic ANN—you can add more layers, use different activation functions, or train it with a dataset using loss functions and optimizers!

ANN 4