## Linear algebra

### Convolutional Neural Networks

Note that we're using the GPU linear algebra provider to train this network.

### Introduction to Bright Wire

Once your machine learning models have been trained you can execute them using highly optimised linear algebra libraries such as the Intel MKL library - or even on the GPU - all from within the same library. This means that any machine learning algorithm that uses linear algebra can run on either the CPU or GPU. Bright Wire supports GPU based machine learning by implementing every linear algebra operation twice - once for the CPU and once for the GPU. The base library includes all machine learning algorithms and CPU based linear algebra:

### Classification Overview with Bright Wire

To use other algorithms we will need to create a linear algebra provider. Naive Bayes and Decision Trees aren't linear algebra based machine learning algorithms (they don't use vectors or matrices). If you have a NVIDIA GPU you can also add GPU based linear algebra with the CUDA add on. Bright Wire supports CPU based linear algebra. However this is such a small data set that the CPU based linear algebra will be fine.