Python Causal Impact Implementation Based on Google's R Package. Built using TensorFlow Probability.

Statistical Rethinking (2nd Ed) with Tensorflow Probability

Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.

TensorFlow Probability Tutorial

Machine learning model for predicting Serie A players performance in a match, in terms of Fantacalcio (italian fantasy football) scores.

This repo contains the notebooks that is used in Medium posts.

A Python package for adding uncertainties to neural network models of chemical systems.

A minimal implementation of a VAE with BinConcrete (relaxed Bernoulli) latent distribution in TensorFlow.

A normalizing flow using Bernstein polynomials for conditional density estimation.

Distributed Training of Bayesian Neural Networks at Scale

Built a regression model that predicts the expected days of hospitalization time and an uncertainty range estimation.

Implementing a bayesian neural network in TensorFlow

Tensor utilities, reinforcement learning, and more!

TensorFlow ML - an abstract implementation of commonly used machine learning algorithms using TensorFlow. Feel free to contribute! (Work in Progress)

Keras, Tensorflow eager execution implementation of Neural Processes

A Bayesian Convolutional Neural Network model for classifying Cataract in Ocular Disease with measurements of uncertainty

Jupyter Notebooks that show the basic functionalities of edward2

Keras, Tensorflow eager execution implementation of Categorical Variational Autoencoder

Code accompanying my 2021 ASA SDSS paper

A generalized framework for generative and probabilistic modelling for training reinforcement learning agents in TensorFlow 2.