The term “federated learning” was coined to describe a form of distributed model training where the data remains on client devices, i.e., is never shipped to the coordinating server. In this post, we introduce central concepts and run first experiments with TensorFlow Federated, using R.
To address the inadequacy of post-hoc explainability tools in XAI for high stake applications, we developed inherently interpretable machine learning models including Deep ReLU networks and Functional ANOVA-based ML models.
In this episode, we talk with Guy Martin, the Executive Director at OASIS. Guy is a globally recognized open source strategy expert.
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.
We will cover often-overlooked concepts vital to NLP, such as Byte Pair Encoding, and discuss how understanding them leads to better models.
In this episode of Open Source Directions, we were joined by Yetunde Dada talked about the work being done with Kedro. Kedro is an open-source Python framework that applies software engineering best practices to data and machine-learning pipelines. You can use it, for example, to optimize the process of taking a machine learning model into a production environment. You can use Kedro to organize a single-user project running on a local environment, or collaborate in a team on an enterprise-level project.
