A setting you choose before training begins, as opposed to parameters the model learns during training.
A setting you choose before training begins, as opposed to parameters the model learns during training. Learning rate, batch size, number of layers, and dropout rate are all hyperparameters. Tuning them correctly can be the difference between a model that works and one that doesn't.
The process of teaching an AI model by exposing it to data and adjusting its parameters to minimize errors.
A hyperparameter that controls how much the model's weights change in response to each update.
The number of training examples processed together before the model updates its weights.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
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