Greetings! I'm Felipe, 23 y.o, I have a deep passion for astrophysics, computer science, artificial intelligence, calculus and the geek universe. I also loves coding, especially creating mobile applications with Dart/Flutter and Kotlin, creating Minecraft plugins with Kotlin and to creating automations and some usefull scripts with Python. I also had experience with college projects using libraries such as Tensorflow, Keras, ScikitLearn, Numpy and Pandas for data analysis or AI predictions and some projects for signal analysis using Discrete Fourier Transforms (DFT). During my tech journey I'm trying to collaborate with what I can by writing tech articles and sharing my knowledges.
I'm also delving deeper into studies in Python, Differential Calculus, Linear Algebra, Artificial Intelligence, Machine Learning and Neural Networks.
- 📌 Salvador Bahia, Brazil
- 🌴 Love anime, K-Pop, J-Pop, Geek culture, basketball, games 👾, coffee ☕, reading 📚 and drawing 🎨
- 💚 I'm a big Pokémon franchise fan
- 🏃🏻♂️ I'm also an amateur runner
- 🌱 I'm currently delving deeper into studies on Artificial Intelligence & Machine Learning area
- 📫 You can reach me at feliper.dev@gmail.com
- 📝 Read my articles at dev.to
- 📖 Current reading: Introdution to Machine Learning - Ethem Alpaydın
🤔 Stuff to study and explore a bit more
- Flutter4Noobs
- Markdown Editor - Flutter Package
- Minecraft Plugins
- This is a project which I "play" a little with Bukkit/Spigot Minecraft Plugins creation with Kotlin. On it I connect a server with a MySQL database using Dart Shelf package and doing requests with Kotlin Retrofit.
- CraftMentor
- At this project I setup a Minecraft Bukkit server in a Docker Container with a PostgreSQL in another Docker Container connected with a Dart-Shelf backend running at own localhost. This backend interacts with a Minecraft Bukkit plugin built with Kotlin.
- TrAIsh Detector (Project url will be available soon)
- At this project I used Python and data processing frameworks and Flutter to create an app that recognizes with the camera some eletronic trash (E-Trash) based on trained samples for an AI model using Tensorflow Lite (TFlite).
🏆 Achievements and stats
