#Machine Learning

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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...

#Integrations

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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...

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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...

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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...

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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...

#Machine Learning

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Nikolas Laskaris |

To view the code, training visualizations, and more information about the python example at the end of this post, visit th...

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Nikolas Laskaris |

  Sections Introduction to NLP Dataset Exploration NLP Processing Training Hyperpa...

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Gideon Mendels |

  This tutorial is Part II of a series. See Part I here. Successfully executing machine learni...

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Gideon Mendels |

How to use Ludwig and Comet.ml together to build powerful deep learning models right in your command line — using an example text classification model   Ludwig is a Tens...

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Gideon Mendels |

Classifying fruits using a Keras multi-class image classification model and Google Open Images   ...

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