PerceptiLabs Blog

A New Visual Approach to Machine Learning Modeling

Oct 8, 2020 3:08:45 PM / by PerceptiLabs posted in Transparency, Machine Learning, Technical Information, Explainability, Modeling Tool

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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.

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Venture Beat: Getting inside the head of a machine learning scientist

Oct 6, 2020 12:51:32 PM / by PerceptiLabs posted in Machine Learning, Media, Model Management, Explainability, Modeling Tool

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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.

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Join us at Red Hat's AI/ML: Smart apps, easy delivery, fast platform webinar series

Jun 4, 2020 9:48:48 AM / by PerceptiLabs posted in Transparency, Machine Learning, Model Management, Modeling Tool, Red Hat

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PerceptiLabs Releases Free Source Code for Our Machine Learning Handbook

May 20, 2020 1:16:57 PM / by PerceptiLabs posted in Machine Learning, Model Management, Model building, Modeling Tool

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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.

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Four Common Types of Neural Network Layers (and When to use Them)

May 20, 2020 1:04:11 PM / by PerceptiLabs posted in Machine Learning, Model Management, MLOps, Explainability, Model building, Modeling Tool, Hyperparameters

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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.

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PerceptiLabs Enterprise now available through Red Hat Marketplace

Apr 28, 2020 8:28:13 AM / by PerceptiLabs posted in News, Modeling Tool, In the News, Red Hat

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PerceptiLabs' Top 5 Open Source Datasets for Machine Learning

Apr 22, 2020 3:25:08 PM / by PerceptiLabs posted in Machine Learning, Modeling Tool, datasets

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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. 

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Five Common Hyperparameters (and how to set them in PerceptiLabs)

Apr 15, 2020 9:05:31 AM / by PerceptiLabs posted in Machine Learning, Technical Information, Modeling Tool, Hyperparameters

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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.

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A Look at PerceptiLabs’ Browser-Based Architecture

Mar 23, 2020 9:49:50 AM / by PerceptiLabs posted in Machine Learning, Model Management, Technical Information, Modeling Tool

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

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The Importance of Transparency in Machine Learning Models

Jan 29, 2020 7:59:00 PM / by PerceptiLabs posted in Transparency, Machine Learning, Explainability, Modeling Tool

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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.

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