Optimize Hyper-parameters [Implementing Generative Model]

[sarahnourgh]

• Tune the hyperparameters of the generative model, such as learning rate, batch size, number of layers, and hidden units, to improve performance and convergence speed.
• Utilize techniques like grid search, random search, or Bayesian optimization to efficiently search the hyperparameter space.

⚠ Remark:

• Debug the code to identify and fix any errors, bugs, or inconsistencies in the Rudy Morel implementation or our team
• Provide clear instructions on how to run the code, train the model, and reproduce the results:
• Document the codebase by adding comments, docstrings, and README files to explain the purpose, functionality, and usage of each component.
• Provide clear instructions on how to run the code, train the model, and reproduce the results.
• Peer Review and Collaboration:
• Collaborate with team members to review each other's code, provide feedback, and ensure code quality, consistency, and adherence to best practices.
  Conduct code reviews, pair programming sessions, and regular meetings to discuss progress and address any issues or concerns.

🥅 The Goal:
Implement successfully the generative model described in the research article and lay the foundation for further experimentation, evaluation, and application in your financial modeling project.