InTDS ArchivebyJavier Rodriguez Zaurin·Jun 13, 2021pytorch-widedeep, deep learning for tabular data IV: Deep Learning vs LightGBM
InTDS ArchivebyJavier Rodriguez Zaurin·Feb 22, 2021pytorch-widedeep: deep learning for tabular dataA flexible package to combine tabular data with text and imagesA response icon2A response icon2
InTDS ArchivebyJavier Rodriguez Zaurin·Jun 19, 2020RecoTour III: Variational Autoencoders for Collaborative Filtering with Mxnet and PytorchA deep dive into the use of Variational Autoencoders for Collaborative Filtering
InTDS ArchivebyJavier Rodriguez Zaurin·Jan 4, 2020Predicting 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 scores
InTDS ArchivebyJavier Rodriguez Zaurin·Oct 6, 2019LightGBM 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…A response icon3A response icon3
InTDS ArchivebyJavier Rodriguez Zaurin·Sep 11, 2019RecoTour II: neural recommendation algorithmsAdding Deep Learning-based recommendation algorithms to RecoTour, a tour through recommendation algorithms in python.A response icon2A response icon2
InTDS ArchivebyJavier Rodriguez Zaurin·Mar 11, 2019Putting ML in production II: logging and monitoringCombining MLflow and hyperparameter optimization tools to log and monitor all the useful data from ML pipelinesA response icon3A response icon3
InTDS ArchivebyJavier Rodriguez Zaurin·Mar 5, 2019Putting ML in production I: using Apache Kafka in Python.Using a message broker to productionise algorithms in real timeA response icon1A response icon1
InDataDrivenInvestorbyJavier Rodriguez Zaurin·Nov 29, 2018RecoTour: 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…
