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Five GANs for Better Image Processing

Machine learning practitioners are increasingly turning to the power of generative adversarial networks (GANs) for image processing. Applications that really benefit from using GANs include: generating art and photos from text-based descriptions, upscaling images, transferring images across domains (e.g., changing day time scenes to night time), and many others.

Machine Learning

Don’t Start your Model from Scratch - Use Transfer Learning

Did you ever wish you could use one of your existing models as a starting point to solve a similar problem? Then you might want to try Transfer Learning. Transfer learning means to use or adapt an existing machine learning (ML) model for a new purpose and helps get you

Top Five Ways That Machine Learning is Being Used for Image Processing and Computer Vision

The field of digital image processing is increasingly shifting from deterministic mathematical calculations and statistical methods, to machine learning (ML)-based approaches which can provide better and more accurate results. This in turn is also helping to drive new methods and use cases for computer vision in the pursuit of

Machine Learning

Recreating Nvidia’s PilotNet in PerceptiLabs

Computer Vision is a key technology for building algorithms to enable self-driving cars. One of the pioneering projects in this field was an experimental system called PilotNet by Nvidia. It uses a deep neural network (DNN) that takes image frames from cameras mounted on the front of a car and

What’s next? Machine Learning 2021

Ah 2020! From global healthcare issues to revolutions in how technology is being adopted and even repurposed, it has been quite a year. At the end of each year, it's always fun to pause and think about machine learning (ML) trends which have seen phenomenal growth, especially around tools, resources,

Machine Learning

Going Beyond the Repo: How Contributing to GitHub can Help Your Career Growth in Machine Learning

Version control tools have long been a staple for information workers, especially developers who need a place to store and collaborate on their code base, while maintaining a complete history of changes. Over the years many such tools have come and gone, or at least fallen out of flavour, but

Machine Learning

Live Coding: TensorFlow vs. PerceptiLabs: Who has more Flower Power

Ever wondered how PerceptiLabs' visual approach compares to writing TensorFlow code? Recently, Robert Lundberg, CTO and Co-Founder of PerceptiLabs, gave a live coding demonstration to show just that, using TensorFlow’s flower classification model. TensorFlow, the most popular machine learning (ML) framework today, provides a tutorial on how to classify

Transparency

An Overview of TensorFlow and how PerceptiLabs Makes it Easier

In A New Visual Approach to Machine Learning Modeling, we talked about how TensorFlow is one of the most popular machine learning (ML) framework today, but it’s not necessarily an easy one for beginners to start building ML models. That's why we decided to create a GUI on top

Machine Learning

Today's Top Four Machine Learning Modeling Challenges and how PerceptiLabs Solves Them

Machine learning (ML) modeling is challenging – we know from experience! From wrangling data to choosing an appropriate ML algorithm, and then debugging and iterating on it, it can be a daunting task to create or update your model. Solving these types of issues is what fueled us to create PerceptiLabs,

Transparency

A New Visual Approach to Machine Learning Modeling

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

Machine Learning

Using ResNets to Detect Anomalies in Industrial IoT Textile Production

Machine learning models for image classification often use convolutional neural networks (CNNs) to extract features from images while employing max-pooling layers to reduce dimensionality. The goal is to extract increasingly higher-level features from regions of the image, to ultimately make some kind of prediction such as an image classification. To

Machine Learning

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

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. https://venturebeat.com/2020/10/

Machine Learning

Announcing PerceptiLabs' October 2020 Silver 0.11 Release

PerceptiLabs is proud to announce the first major Silver release of our visual machine learning (ML) modeling tool, PerceptiLabs 0.11. Not only does this release candidate include a number of significant new features, functionality, and UI improvements, it also offers more stability than our past beta versions. Through these

Machine Learning

Exploring Generative Adversarial Networks (GANs)

A generative adversarial network (GAN) is a powerful approach to machine learning (ML). At a high level, a GAN is simply two neural networks that feed into each other. One produces increasingly accurate data while the other gradually improves its ability to classify such data. In this blog we'll dive

Transparency

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

https://www.brighttalk.com/webinar/build-a-machine-learning-model-with-transparencyWe’re excited to announce we’ll be presenting a session at the upcoming Red Hat webinar series AI/ML: Smart apps, easy delivery, fast platform on Wednesday, June 17. This webinar series will provide a variety of perspectives on driving innovation by simplifying the

Machine Learning

PerceptiLabs Releases Free Source Code for Our Machine Learning Handbook

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

Machine Learning

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

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

News

PerceptiLabs Enterprise now available through Red Hat Marketplace

PerceptiLabs Enterprise is now available through Red Hat Marketplace as an OpenShift Operator. The recently introduced Red Hat Marketplace is a simpler way to buy and manage enterprise software, with automated deployment to any cloud. Built in partnership by Red Hat and IBM, Red Hat Marketplace is designed to meet

Machine Learning

PerceptiLabs' Top 5 Open Source Datasets for Machine Learning

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. Access to open source datasets brings a number of benefits. For starters, it allows you

Machine Learning

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

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

Machine Learning

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

PerceptiLabs is proud to announce our partnership with Red Hat, for our "Enterprise" version of our visual machine learning modeling tool. Through this partnership, our enterprise customers can install PerceptiLabs to run on either their on-premise or cloud-based deployments of the Red Hat OpenShift Container Platform, which hosts an ecosystem

Machine Learning

A Look at PerceptiLabs’ Browser-Based Architecture

During our initial development of PerceptiLabs Beta, we generated our visual modeling tool as a native, platform-specific executable for Windows, Mac, and Linux. We recently switched to a new “browser-based” version that runs a local copy of our kernel on your machine. So, we thought we’d take a few

In The News

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

PerceptiLabs recognized for achievements in AI Model Development New York, NY, March 3, 2020 -- CB Insights today named PerceptiLabs to the fourth annual AI 100 ranking, showcasing the 100 most promising private artificial intelligence companies in the world. “It's been remarkable to see the success of the companies named

Model Management

MLOps: An “Ops” Just for Machine Learning

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

Transparency

The Importance of Transparency in Machine Learning Models

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. AI is inherently non-deterministic in nature,