1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
/*!
* If this is your first time using Easy ML you should check out some of the examples
* to get an overview of how to use matrices or tensors then check out the
* [Matrix](matrices::Matrix) type or [Tensor](tensors::Tensor) type for what you need.
*
* `Matrix` is a straightforward 2 dimensional matrix with APIs built around the notion of
* rows and columns; `Tensor` is a named tensor with full API support for 0 to 6 dimensions.
* Naturally, a 2 dimensional tensor is also a matrix, but the APIs are more general so may
* be less familiar or ergonomic if all you need is 2 dimensional data.
*
* # Examples
* - [Linear Regression](linear_regression)
* - [k-means Clustering](k_means)
* - [Logistic Regression](logistic_regression)
* - [Naïve Bayes](naive_bayes)
* - [Neural Network XOR Problem](neural_networks)
*
* # API Modules
* - [Matrices](matrices)
* - [Named tensors](tensors)
* - [Linear Algebra](linear_algebra)
* - [Distributions](distributions)
* - [(Automatic) Differentiation](differentiation)
* - [Numerical type definitions](numeric)
*
* # Miscellaneous
* - [Web Assembly](web_assembly)
* - [SARSA and Q-learning using a Matrix for a grid world](sarsa)
* - [Using custom numeric types](using_custom_types)
*/
pub mod differentiation;
pub mod distributions;
pub mod interop;
pub mod linear_algebra;
pub mod matrices;
pub mod numeric;
pub mod tensors;
// examples
pub mod k_means;
pub mod linear_regression;
pub mod logistic_regression;
pub mod naive_bayes;
pub mod neural_networks;
pub mod sarsa;
pub mod using_custom_types;
pub mod web_assembly;