##Term
Training Epoch (ˈtreɪnɪŋ ˈɛpɒk)
##Definition
A training epoch is a full cycle through the entire training dataset during the development of a machine learning model. It improves the accuracy of the model by adjusting its parameters repeatedly.
###Where you'll find it
In AI platforms like TensorFlow or PyTorch, you'll encounter training epochs during the model training process. This term appears often in settings or logs related to training sessions and is found across various versions and templates where machine learning model development is supported.
###Common use cases
- Improving Model Accuracy: Iteratively refining model parameters through multiple epochs to increase predictive accuracy.
- Performance Evaluation: Assessing the model's performance after each epoch to monitor progress and make necessary adjustments.
- Hyperparameter Tuning: Deciding the number of epochs as part of the model configuration to balance underfitting and overfitting.
###Things to watch out for
- Overfitting: Too many epochs can lead the model to memorize the training data, reducing its ability to generalize to new data.
- Underfitting: Too few epochs might result in an undertrained model that performs poorly even on training data.
- Computational Cost: Each epoch requires computational resources; more epochs mean more time and higher costs.
###Related terms
- Loss Function
- Model Weights
- Hyperparameter
- Backpropagation
- Training Data