Knowledge Graph — Coursera Notes › Academic disciplines › Information Technology / Computer Science › Artificial Intelligence › Deep Learning
Model Training
concept · part of Deep Learning
The neural network is trained for 50 epochs with a batch size of 32. Performance is tracked on both training and validation sets to monitor learning and generalization.
For example, in a customer churn prediction model, if validation accuracy stagnates while training accuracy rises, monitoring reveals overfitting in real time, enabling interventions like dropout or early stopping. Logging hyperparameters and experiment configurations aids in reviewing past results to guide adjustments.
Inside Model Training (2)
- Model Training Monitoring — Monitoring and logging are essential for successful machine learning model training.
- Fine-tuning — The process of adapting a pre-trained model to a specific task using additional training data.
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