Dhruv Nair |

In this post, we will showcase a Custom Panel dedicated to debugging object detection models. We will use the Penn-Fudan Pedestrian Detection dataset, along...

Dhruv Nair |

Machine Learning models tend to perform inconsistently across different parts of a dataset. Summary performance metrics such as AUC, and F1, are not enough to identify the parts of the data where a model need...

Dhruv Nair |

Hugging Face provides awesome APIs for Natural Language Modeling. In particular, they make working with large transformer models incredibly easy. These models can be used off-the-shelf for text generation, translation, an...

Dominic Garcia |

Comet recently hosted the online panel, “How do top AI researchers from Google, Stanford and Hugging Face approach new M...

Dominic Garcia |

Comet recently hosted the online panel, “How do top AI researchers from Google, Stanford and Hugging Face approach new ML problems?” This is the second post in a series where we recap the q...

Dominic Garcia |

Comet recently hosted the online panel, “How do top AI researchers from Google, Stanford and Hugging Face approach new ...

Dhruv Nair |

Introduction 3D Histograms or Ridge Plots are a great way to visualize the training progress of your Neural Network. Histogram distributions of the weights, gradients, and activations allow us to g...

Doug Blank |

In Machine Learning, a "model" could be anything. The following pasta machine could be described as a model. It takes "input", and a set of "hyperparameters". Well, at least a couple of hyperparameters: the "cutter" d...

Dhruv Nair |

Introduction A confusion matrix is a visual way to inspect the performance of a classification model. Metrics such as accuracy can be inadequate in cases where there are large class imbalances in the data, a p...

Nikolas Laskaris |

Intro by Niko Laskaris We recently released a code-based custom visualization builder called Custom Panels. As part of the rollout, we’re featuring user stories from some of the awesome Rese...

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