AI GlossaryEpochENTRY — Neural Networks
[Neural Networks]
Epoch.
One complete pass through the entire training dataset during model training.
In-depth explanation
01During 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
02EX. 01
Training for 100 epochs
EX. 02
Early stopping at epoch 50
More in Neural Networks
0301Activation FunctionA mathematical function that determines the output of a neuron based on its weighted input sum.02BackpropagationThe algorithm for calculating gradients of the loss function with respect to network weights.03Batch SizeThe number of training examples used in one iteration of model training.04Neural NetworkA computing system inspired by biological neural networks, consisting of interconnected nodes (neurons).05NeuronA basic computational unit in a neural network that receives inputs, applies weights and activation, and produces output.
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