Juan De Dios Santos

I like data, and writing about it. Pokemon Master, and wannabe photographer.

formula 1

A drenched finale: What fans Googled during the 2023 Monaco Grand Prix

Discovering fan curiosity with Google Trends data: A closer look at the most popular search queries during the 2023 Monaco Formula 1 Grand Prix.

Home Assistant

Rise Up! Building an Alert System for My Standing Desk using Data and Home Assistant

Integrating a microprocessor, Home Assistant, Grafana, InfluxDB, and Telegram for a smarter desk and a healthier workspace

formula 1

Google Trends Insights from the 2023 Miami Formula 1 Grand Prix

Discovering fan curiosity with Google Trends data: A closer look at the most popular search queries during the 2023 Miami Formula 1 Grand Prix.

formula 1

What fans Googled during the uneventful 2023 Azerbaijan Grand Prix

Nothing special.

presentation

Presenting “Baking bread on Sundays makes me happy” at csv,conf,v7

The csv,conf conference describes itself as an event that caters to data enthusiasts worldwide, providing insights on data sharing and analysis. It serves as a platform for researchers, open-source maintainers, industry professionals, and data practitioners to share their data platforms, organizations, discoveries, and stories—such as the one I

formula 1

Red flag: what fans Googled during the 2023 Australian Grand Prix

A red flag I don't mind seeing.

formula 1

Exploring what fans Googled during the 2023 Saudi Arabian Formula 1 Grand Prix

Discover the top F1 search queries during the Jeddah Grand Prix with our Google Trends analysis.

google trends

From DRS to Logan Sargeant: What people Googled during the Bahrain Formula 1 Grand Prix

Discovering what people wanted to know about Formula 1 during the 2023 Bahrain Grand Prix.

data analysis

Analyzing My Reddit Usage

A data-driven approach to achieving my New Year's Resolution of reducing my online time.

presentation

I gave a talk at my alma mater and loved it

An emotional talk from which three points stood out.

Will I draw a Battle VIP Pass? Exploring the probabilities of the Pokémon Trading Card Game

Using math to improve my game.

golang

A quick way to generate Go tests in Visual Studio Code

Click here to generate unit tests.

presentation

Why do I quantify my life with data? - a presentation

Presented at CarpentryCon 2022

Pokemon

How does Twitter feel about Pokemon Legends: Arceus?

Understanding tweets about the game using spaCy and Google Cloud's Natural Language AI API.

I’ve tracked my mood for over 1000 days—a data analysis

Using data to better understand what I did and how I felt

From TensorFlow to Beyond

Presented at DecompileD 2021

Two clicks to cluster and block thousands of spammers–a presentation

Presented at Berlin Buzzwords 2021.

Arduino

How to capture sensor data in an Arduino and read it in Python using PySerial

Getting sensor data out of an Arduino Nano 33 BLE.

A brief introduction to TensorFlow.js—a presentation

Presented at twentysix.

TinyML

Detecting Pokemon on an Arduino using TinyML and TensorFlow

Using colors to predict whether this is Pikachu or Bulbasaur

TensorFlow.js

Deploy a pre-trained TensorFlow.js model using Node in Cloud Run

Serving TensorFlow.js' Toxicity Detection model using Google Cloud.

machine learning

Learnings about handling fraud with data and machine learning

Anomaly detectors, rule-based systems, fallback options, and common sense.

Is a neural network better than Ash at detecting Team Rocket?—a presentation

Presented at PyData Global 2020

TensorFlow.js

My first book, Practical TensorFlow.js

My first book, Practical TensorFlow.js (Apress, 2020), is out! As the name indicates, the book aims to teach TensorFlow.js through practical exercises, which cover algorithms ranging from traditional machine learning to deep learning, e.g., linear regression, k-means, convolutional neural networks, and generative adversarial networks. Through hands-on examples,

machine learning

Trying Android's NNAPI ML accelerator with Object Detection on a Pixel 4 XL

Testing and comparing Android's ML acceleration with a MobileDet model.