Part of the r-tensorflow ecosystem, tfprobability is an R wrapper to TensorFlow Probability, the Python probabilistic programming framework developed by Google. We take the occasion of tfprobability’s acceptance on CRAN to give a high-level introduction, highlighting interesting use cases and applications.
Table of Contents CycleGAN: Unpaired Image-to-Image Translation (Part 3) Configuring Your Development Environment Need Help Configuring Your Development Environment? Project Structure Implementing CycleGAN Training Implementing Training Callback Implementing Data Pipeline and Model Training Perform Image-to-Image Translation Summary Citation Information CycleGAN:…
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In this episode of Open Source Directions, we were joined by Thomas Wiecki once again who talked about the work being done with PyMC. PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
Machine learning advancements lead to new ways to train models, as well as deceive them. This article discusses ways to train and defend against attacks.
We will cover often-overlooked concepts vital to NLP, such as Byte Pair Encoding, and discuss how understanding them leads to better models.
A Python example using diagnostic input variables Photo from Unsplash, by EJ Strat Since I’ve been working with healthcare data (almost 10 years now), forecasting future…
