Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate predictions by analyzing large amounts of data. These networks are structured in layers, each of which transforms input data into ‘features’ that guide the analysis of the next layer.



Summary

Deep neural networks (DNNs) are the foundation of modern AI like large language models (LLMs). They learn to make predictions by processing massive datasets. These networks consist of multiple layers. Each layer transforms the input data into abstract ‘features,’ which are then used by subsequent layers for analysis. This layered processing allows DNNs to extract complex patterns and relationships from the data, ultimately enabling accurate predictions and intelligent behavior.

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