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victorcaquilpan/README.md

Víctor Caquilpan

Hi there,

My name is Victor and I am an enthusiastic ML engineer and data scientist focused on solving environmental problems. Now, my main research is in Re-ID of feral cats (a huge problem in all the planet, because these animal hunt multiple native species). I am a Natural Resources Engineer from The University of Chile (Chile), and a student of Master Degree of Artificial Intelligence in The University of Adelaide (Australia). If you want to contact me, you can do it to my personal email victor.caquilpan@gmail.com🌱.

Tools

Main tools and applications

PyTorch Python R Visual Studio Code MicrosoftSQLServer Power Bi Linux Windows Docker

Interesting Resources

I leave some links that may be of interest to several people.

Python

  • Pytorch Lightning. Main page of this wrapper of Pytorch, very useful to leverage the multiple capabilities of this Deep Learning framework.
  • Sensio - Canal de Python y IA. Very good YouTube channel where different topics related to data science, use of python, Git, artificial intelligence, among others, are explained. Highly recommended, it is in Spanish language.

R

  • Inferential Statistic in Data Science. Very good ebook on inferential statistics with several applied cases and easy to understand examples. Highly recommended.
  • Advanced R. Ebook about the use of R at a slightly more advanced level. very useful to get more out of it.
  • Bayes Rules! An Introduction to Bayesian Modeling with R. Very good ebook to learn about Bayesian statistics with examples in R.
  • Efficient R programming. Ebook dealing with various ways on how to increase script performance, memory usage and other similar topics in R.
  • An Introduction to Statistical Learning. A very good book about statistical topics in R.
  • A Data.table and dplyr tour. Translation of functions from the dplyr world (tidyverse) to data.table. Data.table is a variable type similar to data.frame, but optimized for working with large amounts of data. When we must work with large amounts of data, dplyr can fall short, so data.table can be a very good option.
  • dtplyr/translation If you want to convert code that contains dplyr functions and you need to quickly convert everything to data.table, you can use the dtplyr library to do the conversion quickly and easily.

Miscellaneous

  • Curso de Docker en español. YouTube channel where basic topics about the use of Docker and its configuration for multiple purposes are explained (it is in Spanish).
  • Making Slides with R Markdown. Documentation to learn how to use Sharingan, a useful library to create great presentations in R through RMarkdown.

Pinned

  1. PPGNet-Cat PPGNet-Cat Public

    PPGNet-Cat: re-ID in feral cats using camera trap images

    Jupyter Notebook