user
During the AWS DeepRacer Vodafone Championship, what role did hyperparameter optimization play in maximizing the performance of your ML models, and how did you ensure your models generalized well to unseen tracks?
assistant
Machine learning models are never complete and it's important that the various implementations continue iterating. The DeepRacer models are no exception, and I had to ensure the best configuration. I also have lots of experience with cloud services, and also with Google's AI Studio and this also meant I used that knowledge to ensure the models would work.I used this AWS DeepRacer opportunity to really demonstrate the power of AI and machine learning and to fully grasp and understand a new skill that has become and will continue to be prevalent in all facets of life. These are exciting times.In the Deepracer, there were lots of hyperparameters that could be tweaked. After doing some tests, I realised that one of the best and most effective things to do, was to improve the hyperparameters and the reward function, to give the best possible chance for the model to work as intended.To ensure that there was effective deployment and stability, it comes down to making some decisions around how to avoid overfitting and use various actions and methods to ensure that happens and works effectively. In the AWS DeepRacer Vodafone Championship, hyperparameter optimization was key to maximizing ML model performance. I focused on tuning hyperparameters and refining the reward function, whilst also balancing the need to address and avoid potential overfitting.