ML Fundamentals
Transformer
The AI architecture behind GPT, Claude, and every major language model, processes all words in parallel using attention.
Definition
A neural network architecture introduced in the 2017 paper "Attention Is All You Need" that processes sequences using self-attention mechanisms. Processes all tokens in parallel rather than sequentially, enabling efficient training on massive datasets.
Why it matters
The single most important architecture in modern AI, powers virtually every major language model and many vision models.
Related terms in ML Fundamentals
Activation Functions
The "switch" inside a neural network that decides whether a neuron should fire, allowing the AI to learn complex non-linear patterns.
Active Learning
A technique where the AI asks humans to label only the most confusing examples, saving time and money on data labeling.
Anomaly Detection
Finding the "weird" stuff in a dataset, like a credit card charge in a foreign country or a broken machine part.
Artificial General Intelligence (AGI)
A hypothetical "super-AI" that can learn and do any intellectual task a human can do, not just one specific thing.
From vocabulary to outcomes
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