📚 Day 1: Neural Networks - Neurons and Layers

Neural networks are the backbone of modern AI — powering everything from voice assistants to self-driving cars. But before diving into complex architectures, let’s understand the two most basic and essential components: neurons and layers.

What Is a Neuron?

In the world of artificial intelligence, a neuron is a computational unit which is inspired by the human brain that takes input data, performs a mathematical transformation, and passes the output forward.

Example:

Imagine a neuron gets three inputs:

• Input 1: x1 with weight: w1

• Input 2: x2 with weight: w2

• Input 3: x3 with weight: w3

Each input is multiplied by a weight, then added together along with a bias, and finally passed through an activation function (like ReLU or Sigmoid).

output = activation((w1 ∗ x1) + (w2 ∗ x2) + (w3 ∗ x3) + bias)

Note: I will explain bias, weights, activation functions more clearly in next articles. Stay Tuned!

So, you can think of a neuron as: Accepting inputs → applying weights → adding bias → Transforming the result via an activation function

What Is a Layer?

A layer is a group of neurons. Neural networks are built by stacking these layers.

• Input layer: Takes in the raw data (like pixel values of an image).

• Hidden layers: Perform the actual computations and pattern extraction.

• Output layer: Produces the final result (like "cat" or "dog" in classification).

You can have:

• Shallow networks: Few hidden layers (e.g., one or two).

• Deep networks: Many hidden layers (hence the term "deep learning").