Neural networks are the computational backbone of modern artificial intelligence. Inspired loosely by the structure of the human brain, they consist of layers of interconnected nodes that process information by adjusting the weight of connections based on training data.
Deep learning, a subset of machine learning, refers specifically to neural networks with many layers — the depth being what allows them to model complex, abstract patterns in data. This architecture underpins everything from image recognition and natural language processing to drug discovery and financial modelling.
The transformer architecture, introduced in the landmark 2017 paper Attention Is All You Need, revolutionised the field and forms the basis of all modern large language models including GPT, Claude, and Gemini.