(1/2) Google released a new foundation model for time series forecasting 🚀
The TimeFM (Time Series Foundation Model) is a foundation model for time series forecasting applications. This pre-trained model was developed by the Google Research team. It joins the recent trend of leveraging foundation models for time series forecasting, which includes Salesforce's Moirai and Amazon's Chronos.
Version 0.12.0 of the skforecast Python library for time series forecasting with regression models was released this week. The release includes new features, updates for existing ones, and bug fixes. 🧵👇🏼
Join #PyData#Pittsburgh for a casual gathering of the local, national, and international PyData community on the sidelines of #PyCon US 2024! Meet up with fellow #DataScience, #MachineLearning, and scientific computing enthusiasts when the world's largest Python conference comes to town.
Today I learnt about Masakhane, a 'grassroots NLP community for Africa, by Africans', helping make sure that the 2000+ languages and related names and cultures in the continent are represented in technology https://www.masakhane.io/#NLP#AI#MachineLearning#language
On June 15th, my colleague Mónica and I from @EA SEED will be presenting some of our work on #MachineLearning tools for #GameAudio at #AESEurope in Madrid. Really looking forward to visiting UPM again!
Would be nice to have a LLM that you can train locally with your organization documentation, to be able to have an interface to easily find that information buried in decades of documents #LLM#MachineLearning#documentation#FOSS
Die 1. Sitzung ist wieder ausgewählten Abschlussarbeiten gewidmet:
Julia Pabst untersucht, wie #MachineLearning in der #Epigraphik zur Identifikation von Wappen & Inschriften eingesetzt werden kann & Lukas Germann widmet sich den Herausforderungen der Analyse von #Twitter-Daten.