The author mentions that for image classification models, approximately 100 epochs for 10,000 items seem to yield the best results for certain datasets. However, there is a point where continued training may lead to underfitting or overfitting, and no amount of additional training or processing power can enhance the model's performance.
Key takeaways:
- More processing power does not necessarily improve a model, as models can be trained on CPUs with the same results, albeit at a slower pace.
- The quality of a model is determined by how well the dataset fits the model architecture and the amount of time it has been given to reach a semi-accurate prediction ratio.
- For image classification models, around 100 epochs for 10,000 items seems to be the optimal point for certain datasets.
- There is a point where continued training of the model results in underfitting or overfitting, and no amount of additional training or processing power can improve it.