ML Fundamentals
Recurrent Neural Network (RNN)
A neural network designed for sequences, it has a "memory" that processes data one step at a time, remembering what came before.
Definition
A class of neural networks that maintain hidden state across sequence steps, making them suitable for sequential data like text and time series. Largely superseded by Transformers for NLP.
Why it matters
Historically important for language and time-series tasks, understanding RNNs explains why Transformers were a breakthrough.
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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