Neural Network Simplified

IImagine you have images, and you want the robot to tell whether an image is of a cat or a dog. At first, it might make mistakes, but when you say, "Good job!" or "Oops," it learns. It keeps trying until it gets really good at telling animals apart. Just like when you learn to ride a bike, the robot improves with practice!

So, how do we make a robot like that? We can use a neural network. A neural network is a computer program made up of artificial layers of artificial neurons that process and learn from data.

What does that mean? It means inside the robot's "brain," there are layers of neurons. Imagine a layer is like a board, and a neuron is like a knob. These knobs are interconnected and adjust as the robot learns. But again, this is a simplification. In reality, these are artificial neurons, meaning they’re not actual physical neurons. They're just a mathematical model to make things easier to understand.

The Anatomy

A neural network system consists of three key components: neurons, layers, and activation functions. Let's explore each of these step by step.

1. Neurons

Neurons act as containers for learning, much like brain cells. Think of them as intelligent units, each with a specific role.

In our cat-dog image recognition example, one neuron might specialize in recognizing ear shapes, another in identifying jaws, and another in assessing the legs. When the robot encounters an image, these neurons work together.

Specifically, Each neuron has an activation, which acts as a vote to make a decision. For example, let’s say "dog" is 1 and "cat" is 0. If the image has a cat-like ear, the neuron specialized in ear recognition will vote closer to 0.

2. Layers

Layers are collections of neurons at a specific stage of computation in a neural network. Think of them as stages of thinking.

In our image recognition example, the process of identifying body features could be one layer. Suppose the robot needs to analyze the connections between these body features, like the relationship between the jaw and ear, or the ear and leg. Different configurations might produce different images, right? So, we add another layer to the model, representing one stage of thinking. This process can involve many layers.

The first layer is called input layer, the last layer that give a decision is called output layer, and the between layers is called hidden layers.

3. Weights and Activation Function

How do neurons collaborate? Each layer (a stage of thinking) consists of multiple neurons that give their votes. To pass these votes to the next layer or the output layer, we use an activation function. First, we assign a weight to each vote and then sum them up. The activation function processes this sum to decide whether the information should be passed forward.

This function can be simple, like a threshold that outputs 1 if the sum exceeds a certain value and 0 otherwise. Or, it can be more complex, like the sigmoid function, which smooths the output into a range between 0 and 1, making the network more flexible in learning nuanced patterns.

Summary

Neural networks are built from interconnected neurons organized into layers, each equipped with weights and activation functions. Together, these components mimic human-like thinking, enabling machines to learn and make decisions. Whether classifying images or predicting outcomes, neural networks continuously refine their understanding through feedback and practice.