Model building - PerceptiLabs

Transfer Learning Part II: When to use them in Image Processing

Transfer learning allows you to leverage an existing model by modifying and retraining it to fulfill a new use case. In this blog, we explore some key scenarios for why and when you should choose transfer learning over building a new machine learning model from scratch for image processing.

Machine Learning

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

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

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

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

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

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

Transparency

Drag and Drop your way to a new Machine Learning Model

Introducing PerceptiLabs, a Visual Modeling Tool for Machine Learning In the past four to five years, with the growth of machine learning (ML) we’ve seen the number of available ML frameworks explode. TensorFlow has become a prominent player, especially when paired with languages and frameworks like Python and NumPy.