yashua-ovando's repositories
anything-llm
Open-source ChatGPT equivalent experience for both open and close source LLMs, embedders, and vector databases. Supports unlimited documents, threads, and concurrent users and management all in a very clean UI.
checkout
Action for checking out a repo
d3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
fortuna
A Library for Uncertainty Quantification.
gpt-fast
Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.
graph4nlp_demo
This repo is to present various code demos on how to use our Graph4NLP library.
license-keygen-next
Generate license keys for given company details
LLMCompiler
LLMCompiler: An LLM Compiler for Parallel Function Calling
node-red-test
testing git capabilities
notears
DAGs with NO TEARS: Continuous Optimization for Structure Learning
project-azua
Data Efficient Decision Making
setup-python
Set up your GitHub Actions workflow with a specific version of Python
tigramite
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
typed-dag
Causal discovery with typed directed acyclic graphs (t-DAG). This is a ServiceNow Research project that was started at Element AI.