Machine learning

Notes on paper: Large Language Models as Zero-Shot Conversational Recommenders

Link to paper https://arxiv.org/abs/2308.10053 Notes CRS possesses the potential to: (1) understand not only users’ historical actions but also users’ (multi-turn) natural-language inputs; (2) Provide not only recommended items but also human-like responses for multiple purposes, such as preference refinement, knowledgeable discussion, or recommendation justification.Towards this a typical conversational recommender Read more…

By admin, ago

Notes on Paper: RecMind: Large Language Model Powered Agent For Recommendation

Link to paper https://arxiv.org/abs/2308.14296 Notes The paper propose a novel algorithm, Self-Inspiring, to improve the planning ability of the LLM agent. At each intermediate planning step, the LLM “self-inspires” to consider all previously explored states to plan for next step. Literature survey Architecture Tools they used 1) DB tool1) To Read more…

By Deepanshu Lulla, ago

Exploring Vector Databases

Amid the AI revolution, diverse AI models like large language and generative AI models have come into the limelight. These novel AI models require efficient data processing, achievable using vector embeddings. By providing semantic information to the AI models, they gain a better understanding and can perform complex tasks efficiently. Read more…

By admin, ago

Best Practices for Building Machine Learning Applications

Introduction to Building Machine Learning Applications Building machine learning applications requires a thorough understanding of the fundamentals of machine learning and software development. This section will provide an overview of the key considerations and best practices for building machine learning applications. Best Practices for Data Preprocessing Data preprocessing is a Read more…

By Deepanshu Lulla, ago