easy_ml/
lib.rs

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

easy_ml/
lib.rs
