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

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

Distributed Training of Bayesian Neural Networks at Scale

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

Keras, Tensorflow eager execution implementation of Categorical Variational Autoencoder

Code accompanying my 2021 ASA SDSS paper

Deep reinforcement learning implementations with TensorFlow and TensorFlow probability.

Statistics MSc Project (2020): Audio Source Separation

Implementation of Bayesian Non-negative Matrix Factorization using Variational Inference with TensorFlow Probability

Variational Factorization Machines in TensorFlow and PyTorch

Reinforce-lib is a easy to use, simple to extend reinforcement learning library for Python: https://pos.sissa.it/415/018/pdf.

This code finds an optimal architecture, search for hyperparameters (using OPTUNA code), trains and make predictions using LYMAN alpha part of simulated 1D super massive black hole spectra and converts it to intergalactic medium gas conditions along the line of sight. However, this code easily be utilized for ANY 1D signals using supervised ML.

Statistics and Machine Learning in depth analysis with Tensorflow Probability

Task in belong laboratory (related: https://github.com/chiru1221/LabStudyTask2020)

Assorted useful tensorflow functions and keras layers

This repository has code for the paper Bayesian prior impact assessment for dynamical systems described by ordinary differential equations