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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 type or 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
• k-means Clustering
• Logistic Regression
• Naïve Bayes
• Neural Network XOR Problem

API Modules

• Matrices
• Named tensors
• Linear Algebra
• Distributions
• (Automatic) Differentiation
• Numerical type definitions

Miscellaneous

• Web Assembly
• SARSA and Q-learning using a Matrix for a grid world
• Using custom numeric types

Modules

• differentiation
  (Automatic) Differentiation helpers
• distributions
  Models of distributions that samples can be drawn from.
• interop
  Interopability APIs between Matrix/MatrixView and Tensor/TensorView.
• k_means
  K-means example
• linear_algebra
  Linear algebra algorithms on numbers and matrices
• linear_regression
  Linear regression examples
• logistic_regression
  Logistic regression example
• matrices
  Generic matrix type.
• naive_bayes
  Naïve Bayes examples
• neural_networks
  Neural Network training examples
• numeric
  Numerical type definitions.
• sarsa
  SARSA and Q-learning using a Matrix for a grid world.
• tensors
  Generic N dimensional named tensors.
• using_custom_types
  Using custom numeric types examples.
• web_assembly
  Web Assembly examples