date_range 29/10/2025 19:15

How MCP could make connecting AI systems to tools and data as simple as plugging in a cable.

date_range 21/08/2025 19:15

Retrieval-Augmented Generation (RAG) is a cutting-edge approach that combines the strengths of large language models (LLMs) with external knowledge sources, such as vector databases. By integrating retrieval mechanisms, RAG systems can access up-to-date and domain-specific information, significantly improving the relevance and accuracy of generated responses.

date_range 22/09/2024 14:15

Distributed training is crucial for scaling machine learning (ML) models, especially for tasks involving large datasets or complex architectures. The process splits the training workload across multiple machines or GPUs, enabling faster training and greater efficiency. Here’s a breakdown of the key strategies, tools, and platforms that make distributed training effective.

date_range 23/12/2023 19:15

With the advent of large pre-trained language models like BERT and GPT-3, fine-tuning has emerged as a widely adopted method for transfer learning research. This approach entails customizing a pre-trained model for a specific task through training on a more modest dataset containing task-specific labeled data.

date_range 03/12/2023 00:32

Large language models (LLMs) are language models that can recognize, summarize, translate, predict, and generate content using very large datasets. They take text as input and predict what words or phrases are likely to come next. They are built using complex neural networks and trained on massive amounts of text data.