Why is the validation loss metric important when evaluating a model's performance?
Sunday, 13 August 2023
by EITCA Academy
The validation loss metric plays a crucial role in evaluating the performance of a model in the field of deep learning. It provides valuable insights into how well the model is performing on unseen data, helping researchers and practitioners make informed decisions about model selection, hyperparameter tuning, and generalization capabilities. By monitoring the validation loss
How can overfitting be visualized in terms of training and validation loss?
Saturday, 05 August 2023
by EITCA Academy
Overfitting is a common problem in machine learning models, including those built using TensorFlow. It occurs when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. This leads to poor generalization and high training accuracy, but low validation accuracy. In terms of training and validation loss,
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 1, Examination review
Tagged under:
Artificial Intelligence, Machine Learning, Overfitting, TensorFlow, Training Loss, Validation Loss