This is an open collection of methodologies, tools and step by step instructions to help with successful training and fine-tuning of large language models and multi-modal models and their inference.
Latest version improves compatibility and streamlines migrations from legacy databases to SQL Server and Azure platforms.
This framework provides a comprehensive set of tools and utilities for implementing and experimenting with Extreme Learning Machines using Python and TensorFlow. ELMs are a type of machine learning ...
Abstract: sQUlearn introduces a user-friendly, noisy intermediate-scale quantum (NISQ)-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine ...
Abstract: Federated Learning (FL) is a distributed machine learning paradigm involving multiple clients to train a server model. In practice, clients often possess limited data and are not always ...
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