Revisiting Keras for R

OpenTeams November 17, 2021

It’s been a while since this blog featured content about Keras for R, so you might’ve thought that the project was dormant. It’s not! In fact, Keras for R is better than ever, with two recent releases adding powerful capabilities that considerably lighten previously tedious tasks. This post provides a high-level overview. Future posts will go into more detail on some of the most helpful new features, as well as dive into the powerful low-level enhancements that make the former possible.

Categories: Uncategorized

Tags: Keras, Keras for R

NLP Datasets: How good is your deep learning model?

With the rapid advance in NLP models we have outpaced out ability to measure just how good they are at human level language tasks. We need better NLP datasets now more than ever to both evaluate how good these models are and to be able to tweak them for out own business domains.

OpenTeams June 10, 2020

Tokenizers: How machines read

We will cover often-overlooked concepts vital to NLP, such as Byte Pair Encoding, and discuss how understanding them leads to better models.

OpenTeams January 28, 2020

Introduction to Adversarial Machine Learning

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.

OpenTeams October 16, 2019

PyMC Open Source Development

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.

OpenTeams September 7, 2020

PyJanitor Book Open Source Development

Originally a port of the R package, pyjanitor has evolved from a set of convenient data cleaning routines into an experiment with the method chaining paradigm. Data preprocessing usually consists of a series of steps that involve transforming raw data into an understandable/usable format.

OpenTeams March 12, 2020

Revisiting Keras for R

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