Deep Learning
In plain terms
Machine learning using many-layered neural networks to learn complex patterns.
Going deeper
Deep learning stacks layers of artificial neurons that automatically learn features from raw data, excelling at images, speech, and language. It needs lots of data and compute but reaches state-of-the-art results.
Under the hood
Needs large datasets and GPUs/TPUs; techniques like batch norm, dropout, and residual connections enable training very deep nets that learn hierarchical features.
Examples
Image classification (CNNs), speech recognition, LLMs, TensorFlow, PyTorch.
Think of it like...
Like an assembly line of inspectors where each station spots a more abstract detail, edges, then shapes, then faces, building understanding layer by layer.