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.
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 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
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
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
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.