PerceptiLabs Blog

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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Teaching Your AI to do Powerful Things the Easy Way, with PerceptiLabs and Red Hat

Apr 3, 2020 9:18:16 AM / by PerceptiLabs posted in Machine Learning, Automation, Model Management, MLOps, Red Hat, OpenShift

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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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PerceptiLabs Named to the 2020 CB Insights AI 100 List of Most Innovative Artificial Intelligence Startups

Mar 3, 2020 4:59:15 PM / by PerceptiLabs posted in In the News

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PerceptiLabs recognized for achievements in AI Model Development

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MLOps: An “Ops” Just for Machine Learning

Feb 10, 2020 8:01:00 PM / by PerceptiLabs posted in Model Management, Machine Learning Workflow, MLOps, Explainability

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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. 

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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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