Deep Learning

Deep learning is a branch of machine learning that uses neural networks with many layers ("deep" refers to the number of layers, not profundity). Each layer learns to recognize patterns in the output of the previous layer, building up from simple to abstract concepts.

Why depth matters

• In image recognition: early layers detect edges, middle layers detect shapes, late layers detect whole objects.
• In language models: early layers capture grammar and syntax, deeper layers capture meaning and reasoning.

Deep learning vs traditional machine learning

• Traditional ML (like scikit-learn's random forests) often requires hand-crafted features.
• Deep learning learns features automatically from raw data — pixels, text, audio.
• Deep learning needs more data and compute, but scales to problems traditional methods can't handle.

The same math at every scale A two-layer network you train in your browser and a frontier LLM with hundreds of billions of parameters use the same core principles: forward pass, loss, backpropagation, and gradient descent.