Epoch
One complete pass through the entire training dataset during model training.
In-depth explanation
During training, the model typically sees the data multiple times. Each complete iteration through all training examples is one epoch. Training usually requires many epochs for the model to converge. Too few epochs lead to underfitting; too many can cause overfitting. Monitoring validation loss helps determine when to stop training.
Examples
Related terms
More in Neural Networks
Neural Network
A computing system inspired by biological neural networks, consisting of interconnected nodes (neurons).
Neuron
A basic computational unit in a neural network that receives inputs, applies weights and activation, and produces output.
Activation Function
A mathematical function that determines the output of a neuron based on its weighted input sum.
Backpropagation
The algorithm for calculating gradients of the loss function with respect to network weights.
Batch Size
The number of training examples used in one iteration of model training.
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