Neural Networks and Deep Learning
Neural networks are the engines behind today's most impressive AI.
Inspired by the human brain, a neural network is made of layers of connected nodes (artificial neurons). Each node processes information and passes it to the next layer.
Deep Learning uses many layers (sometimes hundreds). This allows the network to learn complex patterns — early layers detect simple features (edges in images), deeper layers recognize full objects or concepts.
Examples: recognizing faces, understanding speech, or generating images. Breakthroughs like AlexNet in 2012 showed deep learning's power for computer vision.
Training requires huge datasets and powerful GPUs. While powerful, deep learning needs lots of energy and can be hard to interpret (the "black box" problem).
