Lesson 25: Introduction to Neural Networks

Welcome to the cutting edge: Neural Networks and Deep Learning!

While simple models like Logistic Regression are great, sometimes we need to recognize highly complex patterns—like identifying a dog in an image or understanding human language. This is where Neural Networks shine.

The Artificial Neuron

Inspired by the human brain, the building block of a neural network is an artificial neuron. It acts as a tiny decision-maker. It takes inputs, multiplies them by weights (which represent importance), adds a bias, and passes the result through an activation function to decide whether to "fire".

Layers and Deep Learning

Neurons are arranged in layers:

Input Layer: One neuron per feature in your dataset.
Hidden Layers: This is where the magic happens. The more hidden layers you have, the "deeper" the network, allowing it to learn incredibly complex patterns.
Output Layer: Produces the final prediction.

Data flows from the input layer through the hidden layers to the output layer in a process called forward propagation.

Coding Challenge: A Simple Neuron

Let's build a single artificial neuron in Python from scratch!

Create an input x = 2.0.
Set a weight w = 0.5 and a bias b = 1.0.
Calculate the weighted sum: z = (x * w) + b.
Apply a simple ReLU activation function (returns the value if positive, otherwise 0): activation = max(0, z).
Print the activation output!