Logging Histograms, Gradients and Activations with Comet
Introduction 3D Histograms or Ridge Plots are a great way to visualize the training progress of your Neural Network. Histogram…
Debugging Classifiers with Confusion Matrices
A confusion matrix can provide us with a more representative view of our classifier’s performance, including which specific instances it is having trouble classifying.
Introducing Panels: Custom Visualizations for Machine Learning
In the last three years since Comet was founded, our users and customers trained millions of models on anything from…
Investing in AI: Unlocking Profitable Machine Learning with Experiment Management
This post was originally published as a sponsored post by Dell Technologies and Intel on CIO.com. We live in an…
comet.ml announces $4.5 million investment to double-down on Comet Enterprise and Meta Learning Capabilities
Current customers include Ancestry, General Electric, The National Institute of Health and Fortune 100 Companies NEW YORK, NY--comet.ml, the industry-leading…
Predictive Early Stopping – A Meta Learning Approach
Introduction Model training is arguably the most time consuming, and computationally demanding part of the Machine Learning pipeline. Depending on…
New Integration: Comet + PyTorch Lightning
Machine learning practitioners can now use PyTorch Lightning with Comet to speed up research cycles and build better models, faster.…
