Machine learning (ML) tools are exploding and specializing, giving users the option to build and manage their ML models in different ways ranging from writing code, relying on frameworks to using automated solutions, each with their pros and cons. The good news is, PerceptiLabs has developed a next-generation ML tool with our visual modeler that makes model building easier, faster, and accessible to a wider spectrum of users, whether you are an expert or beginner.
We were excited to be featured in Venture Beat, in their Article titled: Getting inside the head of a machine learning scientist. Check it out to see how PerceptiLabs allows you to visualize what data scientists see when they are building a machine learning model.
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.
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.