A Painless Introduction

I have reviewed some of the existing libraries to do Graph Convolutional Neural Networks (GCNN’s) and, although in general they are very good, I always return to DGL because it has excellent documentation and many examples, among other things [1]. Here, I want to share my review of a classic example in the study of GCNN’s, the CORA dataset using of course DGL. The CORA dataset is citation network where nodes are articles and edges are citations between them. The gif below helps to get an intuition of how are the connections at a glance.

CORA graph visualized with Pyvis. GIF by author.

There are 2708 nodes with…

A beginner friendly use of cosine similarity

Whenever you start with machine learning, there are several well known datasets ( Iris dataset for example 🥀 ). Among them there is one for images that is simple, popular and interesting, namely the MNIST dataset which consist of a large number of handwritten digits 📝 [1].

Here we are going to use an incredible simple idea to get information from those images: the cosine of the angle between two vectors. So this is going to be particularly good for newbies in ML or DS 😉. It is likely that you are familiar with the dot product for vectors. In…

The easy way to plot superb graphs.

Image by author.

For a while, I and others in the Streamlit community [1] have been seeking a tool to render interactive graphs, but until now this has been something only a few can achieve. This can be due to the fact that a level of expertise with javascript is required, something that many Streamlit users may want to avoid, as this is the promise of the project, deploy beautiful web apps from python, no javascript required!! (of course, mastering javascript is a big plus for making awesome streamlit apps).

Recently I found Pyvis, after a few weeks looking for an alternative to…

Setting up Autodock Vina

Render by author

“There is an urgent need to share our methods, models, and results openly and quickly to test findings, ensure reproducibility, test significance, eliminate dead-ends, and accelerate discovery. Sharing of data for COVID-19 applications will help connect scientists across the global biomolecular simulation community and also improve connection and communication between simulation and experimental and clinical data and investigators.”

A Community Letter Regarding Sharing Biomolecular Simulation Data for COVID-19 [1].

What I present below is the first of at least two posts dedicated to a technique called Virtual Screening used in computational molecular biochemistry. Broadly speaking, it…

How to map a map into graph convolutional networks.

Schematics of a simple Graph Convolutional Neural Network for Mexico’s states graph.

Note from the editors: Towards Data Science is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To learn more about the coronavirus pandemic, you can click here.

First, I want to say that it seems to me of enormous importance that online media gives the opportunity to present to the world the efforts being made by the scientific-technological communities in times as urgent as those we currently live in. It is necessary to disseminate…

Photo by Markus Spiske

Mexico is still in the second phase of the spread of the SARS-Cov-2 virus and has had a very valuable opportunity when observing what has happened in other countries where today the situation is critical.
In this exercise, I will show how the cases reported by 1) The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for cases from around the world and 2) from the information provided by the Mexican government for cases reported as positive to the Covid-19 in the Mexican Republic, can be tracked using Pandas and the Plotly Express library to generate interactive maps.



Physics/ML Lecturer at FCQ-UACH. Member of the Streamlit Creators program.

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