Deep Learning

The Attention Mechanism: AI's Selective Focus

Before attention, AI read sentences like scrolling through text — never looking back. Attention lets models connect any word to any other word in a sentence simultaneously. It's the core of every modern AI.

Scaling Laws: Why Bigger Models Are Better (Usually)

OpenAI's 2020 scaling laws paper showed that model performance improves predictably with scale — more data, more compute, more parameters. This insight drove the race to build ever-larger models.

The Transformer: AI's Most Important Invention

In 2017, a Google paper called 'Attention Is All You Need' changed everything. The Transformer architecture it introduced now powers GPT-4, Claude, Gemini — every major AI model in existence.

Neural Networks: Layers of Learnable Logic

A neural network is layers of simple mathematical functions stacked together. Each layer learns increasingly abstract features. It's modelled loosely on the brain, but don't take that analogy too far.

Convolutional Neural Networks (CNNs) Explained

CNNs revolutionised computer vision by learning to detect edges, shapes, and objects from raw pixels — no manual feature engineering needed.

DeepSeek R1: Open-Source Reasoning at Frontier Level

DeepSeek's R1 model trained with a fraction of the compute of US frontier models yet matches or beats them on reasoning benchmarks. The AI world took notice.