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A recurrent neural network is a type of neural network designed to process sequential data by maintaining an internal memory of previous inputs. This makes it naturally suited for tasks where order matters, such as time-series analysis and language processing.
While largely superseded by transformers for language tasks, RNNs remain important for understanding how sequential data processing evolved and are still used in time-series applications.
Early versions of Google's voice typing used RNNs to transcribe speech by processing audio as a sequence of sounds over time.
Neural Network
A neural network is a computing system loosely inspired by the structure of the human brain, composed of interconnected nodes (neurons) organised in layers. Each connection has a weight that adjusts during training, allowing the network to learn complex patterns from data.
Natural Language Processing (NLP)
Natural language processing is a branch of AI that enables machines to read, understand, interpret, and generate human language. It bridges the gap between human communication and computer understanding, covering tasks like translation, summarisation, and sentiment analysis.
Transformer
A transformer is a neural network architecture that processes input data in parallel using attention mechanisms, rather than sequentially like older models. Introduced in 2017, it has become the dominant architecture for language, vision, and multimodal AI systems.
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