Ah, welcome, adventurer! You've stumbled upon the Dragon's Cave, where the only thing more treacherous than the cave's labyrinthine passages is the math behind the dragon's treasure hoard.
To calculate the optimal regularization parameter for your dragon's treasure, you'll need to understand the following variables:
α = 10^(-3)
This is the learning rate for the dragon's gradient descent algorithm. A smaller value means the dragon will learn faster, but may get stuck in local minima.
β = 0.5
This is the regularization strength. A larger value means the dragon will be more conservative and less likely to overfit.
γ = 1.0
This is the L1 regularization parameter. A larger value means the dragon will be more interested in L1 regularization, but may not generalize as well.
Now that you've learned the dragon's secrets, don't forget to explore the L2 regularization parameter!