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Definition: AI weights and biases


A neural network is a "math machine" that learns from examples. It comprises multiple layers of computational units called "nodes" or "neurons" that are mathematically connected to each other with numerical values called 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 neurons, sometimes hundreds of billions, because the larger their vocabulary, the more questions they can answer and the greater their accuracy and reasoning capacity. See large language model, neural network and AI hyperparameter.




Weights and Biases
Nuerons are computational units, not fixed hardware. Weights control the strength of the connections, and the bias adds a fixed value. The activation function is a formula that uses weights and biases to compute the strength of the neuron's output, which goes to every neuron in the next layer. Randomly set at the start, weights are constantly adjusted in the training phase to generate fewer errors (see AI backpropagation). Bias values initially set to zero are adjusted to reduce errors and enable complex patterns.





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