groverpr's Stars
py-why/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.
NickCH-K/causaldata
Packages of Example Data for The Effect
timeseriesAI/tsai
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
osanseviero/ml_timeline
dair-ai/Prompt-Engineering-Guide
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
abhishekkrthakur/diffuzers
a web ui & api for 🤗 diffusers
matheusfacure/python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
safe-graph/GNN-FakeNews
A collection of GNN-based fake news detection models.
amazon-science/fraud-dataset-benchmark
Repository for Fraud Dataset Benchmark
jwkvam/celluloid
:movie_camera: Matplotlib animations made easy
GuansongPang/deviation-network
Source code of the KDD19 paper "Deep anomaly detection with deviation networks", weakly/partially supervised anomaly detection, few-shot anomaly detection, semi-supervised anomaly detection
benedekrozemberczki/awesome-fraud-detection-papers
A curated list of data mining papers about fraud detection.
gradio-app/gradio
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
fredrik-corneliusson/click-web
Serve click scripts over the web
jalammar/jalammar.github.io
Build a Jekyll blog in minutes, without touching the command line.
parrt/msds621
Course notes for MSDS621 at Univ of San Francisco, introduction to machine learning
anirudhshenoy/pseudo_labeling_small_datasets
Pseudo Labeling for Neural Networks and Logistic Regression/SVMs ( Based on "Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks")
laughingman7743/PyAthena
PyAthena is a Python DB API 2.0 (PEP 249) client for Amazon Athena.
collinprather/BDI-2018-JupyterHub
This repo contains the interactive notebooks used in my talk, "Machine Learning from Scratch". If you're a BDI attendee, check the README for help getting the notebook running on your machine.
msarmi9/Sparkle
Promoting medication adherence with ML ✨
aws-samples/aws-fraud-detector-samples
Sample code and datasets for Amazon Fraud Detector
openvenues/libpostal
A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.
openvenues/pypostal
Python bindings to libpostal for fast international address parsing/normalization
catboost/catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
catboost/tutorials
CatBoost tutorials repository
Laurae2/CategoricalAnalysis
Analysis of Categorical Encodings for dense Decision Trees
tqi2/modeleval
A simple tool for generating useful evaluation metrics and plots for machine learning models
aws/sagemaker-python-sdk
A library for training and deploying machine learning models on Amazon SageMaker
selwin/python-user-agents
A Python library that provides an easy way to identify devices like mobile phones, tablets and their capabilities by parsing (browser) user agent strings.
bayesian-optimization/BayesianOptimization
A Python implementation of global optimization with gaussian processes.