Module traits

Module containing trait definitions for machine learning model interfaces.

This module defines traits that establish common interfaces for various machine learning models, particularly focused on regression algorithms and neural networks. These traits provide standardized methods for accessing and manipulating model parameters and behaviors.

Main traits:

• RegressorCommonGetterFunctions - Defines a common interface for accessing regression model parameters such as learning rate, regularization type, and convergence settings.
• Layer - Defines the core functionality that all neural network layers must implement, including forward and backward propagation and parameter updates.
• LossFunction - Provides an interface for computing loss values and gradients in neural networks.
• Optimizer - Defines methods for updating model parameters during training according to specific optimization algorithms.

Traits

Layer

Defines the interface for neural network layers.

LossFunction

Defines the interface for loss functions used in neural network training.

Optimizer

Defines the interface for optimization algorithms.

RegressorCommonGetterFunctions