mininn 0.1.4

A minimalist deep learnig crate for rust.
Documentation 
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//! Contains the different types of layers that can be used in a neural network.
//!
//! A neural network is composed of multiple layers, each of which performs a specific task
//! to transform the input data into the desired output.
//!
//! All layers structs implement the [`Layer`] trait, which provides a common interface for
//! accessing and manipulating the layer's methods.
//!
//! ## Types of layers
//!
//! | Layer          | Description                                                                                                      |
//! |----------------|------------------------------------------------------------------------------------------------------------------|
//! | `Dense`        | Fully connected layer where each neuron connects to every neuron in the previous layer. It computes the weighted sum of inputs, adds a bias term, and applies an optional activation function (e.g., ReLU, Sigmoid). This layer is fundamental for transforming input data in deep learning models. |
//! | `Activation`   | Applies a non-linear transformation (activation function) to its inputs. Common activation functions include ReLU, Sigmoid, Tanh, and Softmax. These functions introduce non-linearity to the model, allowing it to learn complex patterns. |
//! | `Dropout`      | Applies dropout, a regularization technique where randomly selected neurons are ignored during training. This helps prevent overfitting by reducing reliance on specific neurons and forces the network to learn more robust features. Dropout is typically used in the training phase and is deactivated during inference. |
//! | `Flatten`      | Flattens the input into a 1D array. This layer is useful when the input is a 2D array, but you want to treat it as a 1D array. |
//!
mod layer;
mod types;

pub use layer::*;
pub use types::{Activation, Dense, Dropout, Flatten, DEFAULT_DROPOUT_P};