Daily Newsletter — 12th December 2020

Flower is a framework for building federated learning systems. The design of Flower is based on a few guiding principles:

• Customizable: Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each use case.
• Extendable: Flower originated from a research project at the Univerity of Oxford, so it was built with AI research in mind. Many components can be extended and overridden to build new state-of-the-art systems.
• Framework-agnostic: Different machine learning frameworks have different strengths. Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, or even raw NumPy for users who enjoy computing gradients by hand.
• Understandable: Flower is written with maintainability in mind. The community is encouraged to both read and contribute to the codebase.

paperai is an AI-powered literature discovery and review engine for medical/scientific papers. It helps automate tedious literature reviews allowing researchers to focus on their core work. Queries are run to filter papers with specified criteria. Reports powered by extractive question-answering are run to identify answers to key questions within sets of medical/scientific papers.

paperai was used to analyze the COVID-19 Open Research Dataset (CORD-19), winning multiple awards in the CORD-19 Kaggle challenge.

This is not an official style guide for PyTorch. This document summarizes best practices from more than a year of experience with deep learning using the PyTorch framework. Note that the learnings shared come mostly from a research and startup perspective.

This is an open project and collaborators are welcomed to edit and improve the document.

Three main parts of the guide:

• A quick recap of best practices in Python,
• Tips and recommendations using PyTorch.
• Insights and experiences using other frameworks which helped improve our workflow.