In the context of AI, 'weights' and 'biases' are parameters of the artificial neurons in a neural network. 'Weights' are the strength or amplitude of the input signal, which are adjusted during the learning process to minimize the error in the network's output. 'Biases', on the other hand, are additional parameters that allow the activation function (the function that determines whether a neuron should be activated or not) to be shifted to the left or right, which can make the neuron more flexible in fitting the data. Both 'weights' and 'biases' are crucial in the learning process as they are continuously updated and optimized to improve the network's predictions.
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