Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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Updated
Jun 8, 2024 - Python
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
A software package for flexible HPC GPs
Geostatistical processes for the GeoStats.jl framework
Using Linear Algebra Techniques to accelerate Gaussian Process Regression
Efficient global optimization toolbox in Rust: bayesian optimization, mixture of gaussian processes, sampling methods
Estimate Realtime Case Counts and Time-varying Epidemiological Parameters
R interface to 'dgpsi' for deep and linked Gaussian process emulations
Python package 'dgpsi' for deep and linked Gaussian process emulations
Abstract types and methods for Gaussian Processes.
Combining tree-boosting with Gaussian process and mixed effects models
Gaussian processes in JAX.
Efficient approximate Bayesian machine learning
A simple tool to help you with Gaussian calculations
Non-parametric density inference for single-cell analysis.
Gaussian Process Model Building Interface
Gaussian processes in TensorFlow
Plots of Gaussian processes with AbstractGPs and Makie
Reproducible code for our paper "Explainable Learning with Gaussian Processes"
Quantile set inversion [arXiv:2211.01008] — Numerical experiments
This Python program demonstrates applying Gaussian blurring with different kernel sizes and sigma values to an image using OpenCV.
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