Here at PerceptiLabs we love exploring all sorts of machine learning (ML) approaches. And if you've poked around our site in the last little while, you may have come across our Machine Learning Handbook. It's a free resource that you can download and use to become more familiar with approaches like linear regression, decision trees, k-nearest neighbor, support vector machines (SVMs), clustering, and of course, neural networks.

Neural networks (NN) are the backbone of many of today's machine learning (ML) models, loosely mimicking the neurons of the human brain to recognize patterns from input data. As a result, numerous types of neural network topologies have been designed over the years, built using different types of neural network layers.

Those who are developing machine learning (ML) models or just getting into ML for the first time have it good, because never before have so many open source datasets been freely-available to get you started. 

Creating a machine learning (ML) model involves a lot of variables such as deciding what data to analyze, which approach to employ (e.g., a neural network), and what type of result to generate (e.g., probabilities, classifications, etc.). The key parts of an ML model that define its structure and behavior are its hyperparameters.

During our initial development of PerceptiLabs Beta, we generated our visual modeling tool as a native, platform-specific executable for Windows, Mac, and Linux.

PerceptiLabs recognized for achievements in AI Model Development

If you’ve been around software development for a while, you’ve undoubtedly come across numerous terms appended with the word “Ops”, such as “DevOps”, “TestOps”, or “DataOps”. Of these, “DevOps” (short for “development and information-technology operations”) is probably the most well-known. It refers to a set of software development practices that promote automation and cross collaboration between teams of different disciplines, to reduce software delivery times while achieving a desired level of quality. 

Digital systems are only useful if they can be trusted to do their job. With traditional deterministic systems, we could derive a degree of certainty that a system was working correctly through processes such as unit tests, end-user testing, code reviews, and design documentation.