Neural Network & Deep Learning

Neural Networks (NN)

A Neural Network is a computer system inspired by how the human brain works.

• Our brain has neurons (nerve cells) that connect and pass signals.
• A neural network is made of artificial neurons (nodes) connected in layers.

👉 Each “neuron” takes some input, does a small calculation, and passes the result to the next layer. 👉 By combining many layers, the network can learn very complex things.

Example:

To recognize a cat picture 🐱:

• First layer: detects simple shapes (edges, lines).
• Middle layer: detects patterns (eyes, ears, whiskers).
• Last layer: decides “Yes, it’s a cat!” ✅

Deep Learning (DL)

Deep Learning is a special type of Machine Learning that uses very large neural networks with many layers (deep networks).

• Normal Neural Networks = few layers.
• Deep Learning = many layers (that’s why it’s called “deep”).

👉 More layers = the system can understand more complex patterns.

Example:

• Shallow Neural Network → Might just tell if an image has an animal.
• Deep Neural Network → Can tell which animal it is, what breed, and even recognize faces.

Where They’re Used

Neural Networks:

• basic image recognition
• simple pattern detection.

Deep Learning:

• Self-driving cars 🚗
• Voice assistants (Siri, Alexa) 🎙️
• Google Translate 🌍
• Face recognition 👤
• Medical image analysis 🏥

In short:

• Neural Networks = Computer models inspired by the brain.
• Deep Learning = Bigger, more powerful neural networks with many layers — great at solving complex problems like vision, speech, and natural language.