Hi All,
I hope this email finds you well. I would like to ask for some guidance on applying deep learning models. I understand that the choice of architecture and approach can depend greatly on the type of data available and the problem at hand.
When working with traditional machine learning algorithms, there is typically a clear sequence of steps: collecting data, splitting it, performing feature engineering, selecting a model, tuning hyperparameters, and making predictions. However, when applying deep learning architectures or pre-trained models such as FaceNet, YOLO, ResNet, GloVe, LSTM, Transformers, etc., the workflow can vary significantly.
Is there an overall guideline or set of common steps we can follow when working with deep learning models? I understand that the process depends on various factors, but any highlights or recommendations would be greatly appreciated to provide clarity on the standard practices.
Thank you in advance for your time and assistance!
Best regards,
Yosmery
