process is now complete and ready for testing. This section outlines the procedure for preparing neural network models using Transfer Learning principles combined with previously established tools. Two variational autoencoder models are used as donors to create new models, integrating existing layers with additional decision-making layers. The focus is on evaluating models with consistent architecture while utilizing a universal Expert Advisor (EA) template for testing. Testing involves training models in synchronized environments, ensuring compatible training datasets and historical data, crucial for accurate Transfer Learning. This structured approach aims for efficiency and comparability in model performance evaluations.
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