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marcjerome Goto Github PK
Name: Marc Jerome Tulali
Type: User
Location: Philippines
Blog: http://iammarcjerome.com
Name: Marc Jerome Tulali
Type: User
Location: Philippines
Blog: http://iammarcjerome.com
Website for compilation of baptist sermons and others
A book review website made with flask and flask-sqlalchemy
The Web framework for perfectionists with deadlines.
A reusable django app to have an announcement feature in a django web app.
Final Project in CMPSC 116
Flask SocketIO Chatrooms is a simple chat app that allows users to make chatrooms for them to join and send messages
Returns the data of a particular anime list and status in JSON
Google's i18n address data packaged for Python.
While text classification can classify tweets, assessing whether a tweet is related to an ongoing flood event or not based on its text remains difficult. Inclusion of contextual hydrological information could improve the performance of such algorithms. In this study, we designed a multilingual multimodal neural network that can effectively use both textual and hydrological information. The classification data was obtained from the Twitter-streaming API using flood-related keywords in English, French, Spanish and Indonesian. Subsequently, hydrological information was extracted from a global precipitation dataset based on the tweet’s timestamp and locations mentioned in its text. We performed three experiments analyzing precision, recall and F1-scores while comparing a network that uses hydrological information against a network that does not. Results showed that F1-scores improved significantly across all experiments. Most notably, when optimizing for precision the network with hydrological information could achieve a precision of 0.91 while the network without hydrological information failed to effectively optimize. Moreover, this study shows that including hydrological information can assist in the translation of the classification algorithm to unseen languages. Tweets filtered using this network can be used to more effectively organize disaster response, validate and calibrate flood risk models, and task satellites among other applications.
prereq submission to wba solana - @Japarjam
A django website that allows university students to properly recruit their members in a school project through online proposal
scikit-learn: machine learning in Python
solana-key-decoder-cli is a simple command-line tool for decoding Base58 encoded keys and saving them as JSON files. It takes a Base58 encoded key as input, decodes it, and saves the decoded key as a JSON array in a file named key.json.
Demo of SQLAlchemy through flask. A school requirement report.
A simple list project that was coded through Test-driven development.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.