Could you elaborate on your concern regarding 100 epochs being considered excessive? Are you asking in the context of machine learning, specifically neural network training, where epochs represent the number of times the algorithm sees the entire training dataset? Or is this related to a different field where the term "epochs" holds a different meaning? Understanding the context would help me provide a more accurate response. If it's related to machine learning, it's essential to consider factors such as the complexity of the model, the size of the dataset, and the desired performance to determine if 100 epochs are indeed too much.
7 answers
KatanaSword
Wed Sep 04 2024
In the realm of cryptocurrency and finance, the selection of epochs in deep learning algorithms holds significant importance.
Andrea
Tue Sep 03 2024
Setting the number of epochs too high, such as 100, can result in overfitting, where the model performs well on the training data but poorly on new, unseen data.
Sara
Tue Sep 03 2024
Epochs represent the number of times the entire dataset is passed through the neural network during the training process.
henry_miller_astronomer
Tue Sep 03 2024
It is crucial to strike a balance between underfitting and overfitting when determining the optimal number of epochs.
Eleonora
Tue Sep 03 2024
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