Open in app

Sign In

Write

Sign In

Javier Rodriguez Zaurin
Javier Rodriguez Zaurin

328 Followers

Home

About

Published in Towards Data Science

·Jun 13, 2021

pytorch-widedeep, deep learning for tabular data IV: Deep Learning vs LightGBM

A thorough comparison between DL algorithms and LightGBM for tabular data for classification and regression problems — Here we go with yet another post in the series. This is the fourth of the series. The previous three posts, and the original version of this post are hosted in my own blog, just in case. I started planning this posts a few months ago, as soon as I…

Tabular Data

30 min read

Tabular Data

30 min read


Published in Towards Data Science

·Feb 22, 2021

pytorch-widedeep: deep learning for tabular data

This is the third of a series of posts introducing pytorch-widedeepa flexible package to combine tabular data with text and images (that could also be used for “standard” tabular data alone). …

Pytorch

17 min read

pytorch-widedeep: deep learning for tabular data
pytorch-widedeep: deep learning for tabular data
Pytorch

17 min read


Published in Towards Data Science

·Jun 19, 2020

RecoTour III: Variational Autoencoders for Collaborative Filtering with Mxnet and Pytorch

This post and the code here are part of a larger repo called RecoTour, where I normally explore and implement some recommendation algorithms that I consider interesting and/or useful (see RecoTour and RecoTourII). In every directory, I have included a README file and a series of explanatory notebooks that I…

Variational Autoencoder

13 min read

RecoTour III: Variational Autoencoders for Collaborative Filtering with Mxnet and Pytorch
RecoTour III: Variational Autoencoders for Collaborative Filtering with Mxnet and Pytorch
Variational Autoencoder

13 min read


Published in Towards Data Science

·Jan 4, 2020

Predicting Amazon review scores using Hierarchical Attention Networks with PyTorch and Apache Mxnet

This post and the code here are part of a larger repo that I have (very creatively) called “NLP-stuff”. As the name indicates, I include in that repo projects that I do and/or ideas that I have — as long as there is code associated with those ideas — that…

Machine Learning

16 min read

Predicting Amazon review scores using Hierarchical Attention Networks with PyTorch and Apache…
Predicting Amazon review scores using Hierarchical Attention Networks with PyTorch and Apache…
Machine Learning

16 min read


Published in Towards Data Science

·Oct 6, 2019

LightGBM with the Focal Loss for imbalanced datasets

The 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 designed to address scenarios with extreme imbalanced classes, such as one-stage object detection where the imbalance between foreground and background classes can be, for example, 1:1000. …

Machine Learning

7 min read

LightGBM with the Focal Loss for imbalanced datasets
LightGBM with the Focal Loss for imbalanced datasets
Machine Learning

7 min read


Published in Towards Data Science

·Sep 11, 2019

RecoTour II: neural recommendation algorithms

This is the second of a series of posts on recommendation algorithms in python. In the first of the series, that I wrote quite a while ago, I quickly went through a number of algorithms that I implemented and tried using Kaggle’s Ponpare dataset. You can find all the related…

Machine Learning

15 min read

RecoTour II: neural recommendation algorithms
RecoTour II: neural recommendation algorithms
Machine Learning

15 min read


Published in Towards Data Science

·Mar 11, 2019

Putting ML in production II: logging and monitoring

In our previous post we showed how one could use the Apache Kafka’s Python API (Kafka-Python) to productionise an algorithm in real time. In this post we will focus more on the ML aspects, more specifically on how to log information during the (re)training process and monitor the results from…

Machine Learning

15 min read

Putting ML in production II: logging and monitoring
Putting ML in production II: logging and monitoring
Machine Learning

15 min read


Published in Towards Data Science

·Mar 5, 2019

Putting ML in production I: using Apache Kafka in Python.

This is the first of 2 posts where we will illustrate how one could use a series of tools (mostly Kafka and MLFlow) to help productionising ML. To that aim we will set a simple scenario that we hope resembles some real-word use-cases, and then describe a potential solution. …

Machine Learning

10 min read

Putting ML in production I: using Apache Kafka in Python.
Putting ML in production I: using Apache Kafka in Python.
Machine Learning

10 min read


Published in DataDrivenInvestor

·Nov 29, 2018

RecoTour: a tour through recommendation algorithms in python

A while ago a friend of mine asked me about approaches that would be useful when optimizing GBMs. I had been asked this question a few times in the past, so I thought I could share some code and write a post about it, just in case someone finds it…

Machine Learning

9 min read

RecoTour: a tour through recommendation algorithms in python
RecoTour: a tour through recommendation algorithms in python
Machine Learning

9 min read

Javier Rodriguez Zaurin

Javier Rodriguez Zaurin

328 Followers

Scientist

Following
  • Pinterest Engineering

    Pinterest Engineering

  • Logan Kilpatrick

    Logan Kilpatrick

  • PyTorch

    PyTorch

  • Michael Jordan

    Michael Jordan

  • Jeremy Howard

    Jeremy Howard

Help

Status

Writers

Blog

Careers

Privacy

Terms

About

Text to speech