Model Selection & Hyper-Parameter Tuning

In training AI models, we have to choose what kind of model will be trained, e.g. how many layers and how many neurons per layer. This is model selection. The machine learning algorithm that trains the model has parameters such as the learning rate or momentum that need to be chosen. While both can be done manually by selecting a few options and choosing the best one, this process can be automated! In automating it, we need to define precisely what we mean by "best" to navigate the bias-variance-trade-off. Also, we need the training run to be fast. Otherwise, we could never run the many trial-and-error experiments needed. Brightics AI Accelerator does all of this for you - yielding a much better model.

[abhi_and_seki]

Superb explanation!! Absolutely loved it!! 😊 THANKS