Introduction

• A subset of Machine Learning that uses multi-layered neural networks to learn complex patterns from large datasets.
• Excels at images, audio, text, and sequential data .
  • Solve traditional ML struggles.
• Auto learn features, unlike classical ML which often requires manual feature engineering.

Disadvantages:

• Needs large datasets
• Computationally expensive
• Often a black-box (less interpretable)

Neural Network

• A computational model inspired by the human brain.
• Composed of neurons (nodes) connected by weights, arranged in layers.
• Learns complex patterns through training.

Neuron

• Basic unit of a neural network, similar to a biological neuron.