Pinned Repositories
bootsteps
step-by-step tutorial on how to implement basic bootstrap analyses
bsplines
Causal-Inference-1
Causal Inference 1 Mixtape Session taught by Scott Cunningham
functorch
functorch is a prototype of JAX-like composable function transforms for PyTorch.
gaPyKrig
Kriging for GA project
pytorch_geometric
Graph Neural Network Library for PyTorch
pytorch_sparse
PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations
pytorch_geometric
Graph Neural Network Library for PyTorch
botorch
Bayesian optimization in PyTorch
Padarn's Repositories
Padarn/gaPyKrig
Kriging for GA project
Padarn/bootsteps
step-by-step tutorial on how to implement basic bootstrap analyses
Padarn/Causal-Inference-1
Causal Inference 1 Mixtape Session taught by Scott Cunningham
Padarn/functorch
functorch is a prototype of JAX-like composable function transforms for PyTorch.
Padarn/pytorch_geometric
Graph Neural Network Library for PyTorch
Padarn/pytorch_sparse
PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations
Padarn/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Padarn/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.
Padarn/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.
Padarn/flink-on-k8s-operator
Kubernetes operator for managing the lifecycle of Apache Flink and Beam applications.
Padarn/folium
Python Data. Leaflet.js Maps.
Padarn/incubator-airflow
Apache Airflow (Incubating)
Padarn/jsonparser
One of the fastest alternative JSON parser for Go that does not require schema
Padarn/kube-capacity
A simple CLI that provides an overview of the resource requests, limits, and utilization in a Kubernetes cluster
Padarn/kubeflow-aws
Kustomize manifest to deploy kubeflow pipelines in AWS
Padarn/langchain
🦜🔗 Build context-aware reasoning applications
Padarn/notes
Padarn/Open3D
Open3D: A Modern Library for 3D Data Processing
Padarn/OpenSfM
Open source Structure-from-Motion pipeline
Padarn/padarn.github.com
Blog
Padarn/pipelines
Machine Learning Pipelines for Kubeflow
Padarn/probability
Probabilistic reasoning and statistical analysis in TensorFlow
Padarn/pyro
Deep universal probabilistic programming with Python and PyTorch
Padarn/python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and sensitivity analysis.
Padarn/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Padarn/randomizepy
Padarn/serve
Serve PyTorch models in production
Padarn/survival
Survival package for R
Padarn/time-series-dataset
:wrench: Easy-to-use PyTorch Dataset object for multivariate time series :wrench:
Padarn/werkzeug
A flexible WSGI implementation and toolkit