Developers looking to evaluate machine learning methods find a growing array of specialized hardware and development platforms that are often tuned to specific classes of machine learning architecture and application. Although these specialized platforms are essential for many machine learning applications, few developers new to machine learning are ready to make informed decisions about selecting the ideal platform.
Developers need a more accessible platform for gaining experience in development of machine learning applications and a deeper understanding of resource requirements and resulting capabilities.
As described in the Digi-Key article “Get Started with Machine Learning Using Readily Available Hardware and Software”, development of any model for supervised machine learning comprises three key steps:
Preparation of data for training a model
Data preparation combines familiar data acquisition methods with an additional step required to label specific instances of data for use in the training process. For the final two steps, machine learning model specialists, until recently, needed to use relatively low-level math libraries to implement the detailed calculations involved in model algorithms. The availability of machine learning frameworks has dramatically eased the complexity of model implementation and training.
Today, any developer familiar with Python or other supported languages can use these frameworks to rapidly develop machine learning models able to run on a wide array of platforms. This article will describe the machine learning stack and training process before getting into how to develop a machine learning application on a Raspberry Pi 3.By Stephen Evanczuk