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Definition: AI weights and biases


A neural network comprises multiple layers of nodes that are mathematically connected to the each other using weights and biases, collectively known as "parameters."

Neural networks are developed to create models for language and vision recognition as well as chatbots, all of which are major applications of AI. Chatbot "language models" have the largest number of parameters, sometimes tens of billions, because the larger their vocabulary, the more questions they can answer. See large language model.

Randomly set at the start, parameters are constantly adjusted in the training phase to generate the best results. See neural network and AI hyperparameter.




Weights and Biases
The weights control the strength of the connections, and the bias adds a fixed value. The activation function is a formula that uses the weights and biases to compute the strength of the neuron's output.





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