What is loss function optimization landscape in supervised learning?

The optimization landscape is the shape of the loss function surface that determines how easily a model can be trained.

The loss function optimization landscape represents how loss changes with respect to model parameters. In simple models like linear regression, the landscape is convex with a single global minimum. In deep learning, the landscape becomes highly non-convex with multiple local minima, saddle points, and flat regions. Gradient-based methods like SGD navigate this landscape iteratively. The geometry of this space strongly affects convergence speed and stability.