When I learn something new I like to challenge myself trying to explain it to somebody else like showing how to get over the learning obstacles I found. Also I think it is a way to better stimulate my recall months later, when I'll have forgotten almost everything 😅.
Here are some of my notes of this kind.
- Einsum - practical notes
- Graph Theory - notes
- Interactive Gradient Descent
- Learning Rate Annealing
- FastAI Image Classifier base line
- FastAI Collaborative Filtering
- Pandas
- AWS Certification - Cloud Practitioner
- Docker by examples
- Jupyter Notebooks versioning with Git
- The Model Evaluation Game A sort of simple board-game rules to play with people who want to better understand the Machine Learning Model Evaluation basics.
- How to create a dataset in Google Colab for your Machine Learning projects
- FastAI course notes
- Deep Learning notes
- Travis for testing
- Data-Forge
- Jest for testing
- mongoose_discriminator
- GraphQL
- JS Promises
Some code that I made for my routines .
- ML code
- pandas utils. Some pandas utility functions.
Here are links to some tools I made, for personal purposes or work.
- flightsfunnel.com a web app to look for the cheapest combination of flights to meet in one place while coming from different oring places.
- Web CV, check the mini projects page
- oro-facial_classifier
- PlayNeurons
- Ombro cinema online, Alpha Tool to create a nice ombroCinema picture. Still in alpha version, need some adjustment. I created this for the master thesis work of a friend of mine, Bilgesu Dogan, Designer.
- vimeoEasy
Neuro & geeknering. It's a fb page that I opened with some friends to publish news about brain, sciences and technologies related to it.