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.
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 enhancements, PerceptiLabs is becoming the GUI for TensorFlow–you drag and drop the components, and PerceptiLabs generates the visualizations and TensorFlow code for you.
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.
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.
Introducing PerceptiLabs, a Visual Modeling Tool for Machine Learning