In machine learning, an epoch refers to one complete pass through the entire training dataset by the learning algorithm. During each epoch, the model updates its parameters to improve its ability to make predictions.
Use Cases
Training Models
Iteratively improving model performance by adjusting parameters after each epoch.
Monitoring Progress
Tracking model accuracy and loss over multiple epochs to assess training effectiveness.
Importance
Optimization
Allows the model to learn from the entire dataset over multiple iterations.
Convergence
Helps determine when the model has reached optimal performance or requires further training.
Fine-tuning
Provides opportunities to adjust learning rates and other parameters for better results.
Analogies
An epoch is like practising a musical piece several times to perfect your performance. Each practice session (epoch) helps you improve your skills (model parameters) until you achieve the desired level of proficiency (model accuracy).
