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This repository houses the datasets/resources used in paper "ChatGPT Informed Graph Neural Network for Stock Movement Prediction". Dive in to find the datasets, code samples, and more!
Code for AAAI'24 paper "Graph Neural Prompting with Large Language Models".
A collection of AWESOME things about Graph-Related LLMs.
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
This is one of Petrobras' open repositories on GitHub. It contains the WPRAutoencoders project which encompasses a wellbore pressure response generator, a dataset of 20.000 synthetic pressure respoโฆ
Spanish word embeddings computed with different methods and from different corpora
Understanding Deep Learning - Simon J.D. Prince
Repo for the Belebele dataset, a massively multilingual reading comprehension dataset.
Label Studio is a multi-type data labeling and annotation tool with standardized output format
๐ ๐ง๐ต๐ฒ ๐๐๐น๐น ๐ฆ๐๐ฎ๐ฐ๐ธ ๐ณ-๐ฆ๐๐ฒ๐ฝ๐ ๐ ๐๐ข๐ฝ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ | ๐๐ฒ๐ฎ๐ฟ๐ป ๐ ๐๐ & ๐ ๐๐ข๐ฝ๐ for free by designing, building and deploying an end-to-end ML batch system ~ ๐ด๐ฐ๐ถ๐ณ๐ค๐ฆ ๐ค๐ฐ๐ฅ๐ฆ + 2.5 ๐ฉ๐ฐ๐ถ๐ณ๐ด ๐ฐ๐ง ๐ณ๐ฆ๐ข๐ฅ๐ช๐ฏ๐จ & ๐ท๐ช๐ฅ๐ฆ๐ฐ ๐ฎ๐ข๐ต๐ฆ๐ณ๐ช๐ข๐ญ๐ด
Library for fast text representation and classification.
Interpretable data visualizations for understanding how texts differ at the word level
VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social โฆ
Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
Example notebooks that show how to apply quantum computing with Amazon Braket.
๐ A ranked list of awesome machine learning Python libraries. Updated weekly.
Active Learning for Text Classification in Python
A collection of awesome readme templates to display on your profile
A collection of tools for processing graphs based on SNAP lib.
skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.
Visually explore, understand, and present your data.
Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.
Code for 'Relational Deep Learning: A Deep Latent Variable Model for Link Prediction' - AAAI
A scikit-learn based module for multi-label et. al. classification
Simple Reinforcement learning example, based on the Q-function.
Reinforcement learning algorithms applied to the Pacman game
List of Data Science Cheatsheets to rule the world
This is the repository for the paper "ANEW for Spanish Twitter Sentiment Analysis using Instance-Based Multi-Label Learning Algorithms"