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Source channel @githubtrending · Post #14993 · Jul 24

#jupyter_notebook Retrieval Augmented Generation (RAG) helps large language models (LLMs) answer questions using up-to-date or private information by connecting them to external data sources, unlike fine-tuning which retrains the model on specific data. RAG is useful when you need current, dynamic information without costly retraining, making it ideal for tasks like customer support or knowledge management. Fine-tuning is better for deep expertise in a specialized field but requires more data and effort. Using RAG lets you get accurate, relevant answers quickly by combining the model’s language skills with fresh, specific data, improving usefulness and reliability. https://github.com/langchain-ai/rag-from-scratch

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DataSci NU

@nu_datasci · Post #3 · 02/18/2023, 01:21 PM

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Attention all Data Science enthusiasts! We're thrilled to announce that our club is kicking off a series of exciting events that cover a wide range of topics in data analytics, computer vision, natural language processing, and machine learning in robotics. First up, we have a speech and Q&A session with Professor Ernazar Abdikamalov, a renowned researcher who uses machine learning in physics. Following that, we'll hear from experts in the industry, including representatives from Kolesa Group and Jusan Group. And to wrap up the series, we're hosting a sponsored hackathon on data analytics! This is a fantastic opportunity to expand your knowledge, network with industry professionals, and put your skills to the test. Stay tuned for more information on the dates and details of each event. Link to our Instagram We can't wait to see you there! #DataSci#DataAnalytics#ComputerVision#NLP#MLinRobotics💻🔬🤖

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