Published inTDS Archivepytorch-widedeep, deep learning for tabular data IV: Deep Learning vs LightGBMJun 13, 2021Jun 13, 2021
Published inTDS Archivepytorch-widedeep: deep learning for tabular dataA flexible package to combine tabular data with text and imagesFeb 22, 2021A response icon2Feb 22, 2021A response icon2
Published inTDS ArchiveRecoTour III: Variational Autoencoders for Collaborative Filtering with Mxnet and PytorchA deep dive into the use of Variational Autoencoders for Collaborative FilteringJun 19, 2020Jun 19, 2020
Published inTDS ArchivePredicting Amazon review scores using Hierarchical Attention Networks with PyTorch and Apache…Using the hierarchical structure of documents and MLP-based attention mechanisms to predict amazon reviews scoresJan 4, 2020Jan 4, 2020
Published inTDS ArchiveLightGBM with the Focal Loss for imbalanced datasetsThe Focal loss (hereafter FL) was introduced by Tsung-Yi Lin et al., in their 2018 paper “Focal Loss for Dense Object Detection”[1]. It is…Oct 6, 2019A response icon3Oct 6, 2019A response icon3
Published inTDS ArchiveRecoTour II: neural recommendation algorithmsAdding Deep Learning-based recommendation algorithms to RecoTour, a tour through recommendation algorithms in python.Sep 11, 2019A response icon2Sep 11, 2019A response icon2
Published inTDS ArchivePutting ML in production II: logging and monitoringCombining MLflow and hyperparameter optimization tools to log and monitor all the useful data from ML pipelinesMar 11, 2019A response icon3Mar 11, 2019A response icon3
Published inTDS ArchivePutting ML in production I: using Apache Kafka in Python.Using a message broker to productionise algorithms in real timeMar 5, 2019A response icon1Mar 5, 2019A response icon1
Published inDataDrivenInvestorRecoTour: a tour through recommendation algorithms in pythonA while ago a friend of mine asked me about approaches that would be useful when optimising GBMs. I had been asked this question a few…Nov 29, 2018Nov 29, 2018
