Deep Netts Core Pro 1.11 API
Core engine configuration and settings.
Data structures and collections to provide example data to build machine learning models, and utility methods for common data manipulation.
Data normalization methods, used to scale data, in order to make them suitable for use by neural network.
Evaluation procedures for machine learning models, used to estimate how good models are performing for a given data.
Neural network architectures with their corresponding builders.
Neural network layers, which are main building blocks of a neural network.
Activation functions for neural network layers.
Commonly used loss functions, which are used to calculate error during the training as a difference between predicted and target output.
Training algorithms and related utilities.
Optimization methods used by training algorithm.
Weights randomization techniques, used for initializing layer's internal parameters.
Various utility classes including Tensor, image operations, multithreading, exceptions etc.