Pinned Repositories
AnDA
¡AnDA! is a Python library for the Analog Data Assimilation.
awesome-uncertainty-deeplearning
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
bayesian-machine-learning
Notebooks related to Bayesian methods for machine learning
DA-tutorials
Course on data assimilation (DA)
Data-Assimilation-Notebooks
emukit
A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
GPflow
Gaussian processes in TensorFlow
GPy
Gaussian processes framework in python
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
HugoLarochelle_NN_Exercises
A repo contains the implementation codes and assignments for Hugo Larochelle's Neural Network Course. http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html
Ahirvoas's Repositories
Ahirvoas/AnDA
¡AnDA! is a Python library for the Analog Data Assimilation.
Ahirvoas/awesome-uncertainty-deeplearning
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
Ahirvoas/bayesian-machine-learning
Notebooks related to Bayesian methods for machine learning
Ahirvoas/DA-tutorials
Course on data assimilation (DA)
Ahirvoas/Data-Assimilation-Notebooks
Ahirvoas/emukit
A Python-based toolbox of various methods in uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
Ahirvoas/GPflow
Gaussian processes in TensorFlow
Ahirvoas/GPy
Gaussian processes framework in python
Ahirvoas/handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Ahirvoas/HugoLarochelle_NN_Exercises
A repo contains the implementation codes and assignments for Hugo Larochelle's Neural Network Course. http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html
Ahirvoas/IEA-15-240-RWT
15MW reference wind turbine repository developed in conjunction with IEA Wind
Ahirvoas/Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Ahirvoas/meghna-hugo
Meghna Hugo is a responsive, minimalist, lightweight, and fast loading one-page business theme built with HTML5/CSS3, Hugo, JavaScript, and JQuery.
Ahirvoas/missingno
Missing data visualization module for Python.
Ahirvoas/openfast
Main repository for the NREL-supported OpenFAST whole-turbine and FAST.Farm wind farm simulation codes.
Ahirvoas/openturns
Uncertainty treatment library
Ahirvoas/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Ahirvoas/pyMAP
python wrapper for MAP
Ahirvoas/Python-for-Probability-Statistics-and-Machine-Learning
Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"
Ahirvoas/python-machine-learning-book
The "Python Machine Learning" book code repository and info resource
Ahirvoas/pyTurbSim
pyTurbSim is a turbulence simulation tool for HydroKinetic (tidal, river and ocean-current) turbine device design tools (e.g. NWTC's FAST). It is also an effective development platform for new TurbSim spectral models.
Ahirvoas/scikit-learn
scikit-learn: machine learning in Python
Ahirvoas/Training-Day1
Ahirvoas/Training-Day2
Ahirvoas/TurbSim
A stochastic, full-field, turbulence simulator
Ahirvoas/WindTurbineOutputPrediction
Work done at the H2O Open Tour NYC 2016 Hackathon, and later refinements