(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. 🧵👇🏼
(2/2) Here are some of the new features:
✅ Ability to forecast multiple series with different lengths and/or different exogenous variables per series.
✅ Bayesian hyperparameter search is now available for all multiseries forecasters using optuna as the search engine.
✅ New forecasting models based on deep learning models (RNN and LSTM)
✅ New methods for creating prediction intervals
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!
@metin Dis yiu test the AI Link to Krita? It works offline and gives also nice results. The option to control the AI reslts is incredible and brings "artist skills back to the desk".
If someone interested in the AI Module for Krita: https://github.com/Acly/krita-ai-diffusion
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.