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
May 20, 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.
Summary notebooks using derivative gaussian processes with tinygp. We implement a 2D derivative gaussian process and successfully use derivatives to regularize SVI fits with a gaussian process model..
A collection of Jupyter notebooks for inference on Imbalance in the EU ETS: a non-parametric approach - C. Salvagnin, A. Glielmo, M.E. De Giuli, A. Mira
Notebooks about Bayesian methods for machine learning
Notebooks and Data for ICA's course on IA
Just a notebook reproducing the Non-linear Autoregressive Gaussian Process (Perdikaris et al, 2017) using Tensorflow Probability
Concepts of Bayesian Statistics, Bayesian inference, computational techniques and knowledge about the different types of models as well as model selection procedures.
This repo consists of Data science Engineering Methods and tools projects' Jupyter notebook and data files.
Simple Experiments mainly on Machine Learning
Jupyter notebooks created for use in a training session on the topic of drought forecasting via satellite:artificial_satellite:. This repo contains the scripts needed to pre-process MODIS data and apply Gaussian Processes to time-series in order to forecast VCI :chart_with_upwards_trend:.
This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.
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