This post is the fourth in a series of introductory tutorials on the Open Neural Network Exchange (ONNX), an initiative from AWS, Microsoft, and Facebook to define a standard for interoperability across machine learning platforms.

In this post, we continue to consider how to speed up inference quickly and painlessly if we already have a trained model in PyTorch. In the previous post We discussed what ONNX and TensorRT are and why they are needed Сonfigured the environment for PyTorch…

Open Neural Network Exchange (ONNX) is an open format built to represent machine learning models. It defines the building blocks of machine learning and deep learning models along with a common file format to enable AI developers to use models with a variety…

Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set of supported…

Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their training...

Microsoft researchers have developed — and released — a hyper-efficient AI model that can run on CPUs, including Apple's M2.