What Epoch Meaning, Applications & Example
One complete pass through the entire training dataset.
What is an Epoch?
In machine learning, an Epoch refers to one complete pass through the entire training dataset during the learning process. In each epoch, the model learns from the dataset by adjusting its weights to minimize error, aiming to improve accuracy in predicting outputs.
Importance of Epochs
Epochs help the model iteratively learn and generalize from the data. A suitable number of epochs allows a model to converge on optimal weights, balancing between underfitting (too few epochs) and overfitting (too many epochs).
Applications of Epochs
- Training Neural Networks: Setting appropriate epoch counts ensures the model learns effectively without overfitting.
- Hyperparameter Tuning : Choosing the right epoch count can improve model performance without excessive computation.
- Cross-Validation : Epochs are often adjusted in combination with other parameters for optimal validation performance.
Example of Epochs
If a dataset has 10,000 samples, and the batch size is 1,000, a single epoch will involve 10 updates. Training a model for 50 epochs means it passes through the dataset 50 times, continually adjusting weights to improve accuracy.