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 Deepanshu Lulla, ago

Machine Learning Engineering by Andriy Burkov by Chapter 1 notes

Machine learning can also be defined as the process of solving a practical problem by,1) collecting a dataset, and2) algorithmically training a statistical model based on that dataset Learning can be supervised, semi-supervised, unsupervised, and reinforcement. Supervised Learning In supervised learning the data analyst works with collection of labelled examples Read more

By Deepanshu Lulla, 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

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

Distribution of error functions

We can plot error distributions like probability density functionand cumulative density function and make important deductionsbased on it. We can use plot Probability Density functions(PDF) and Cumulative density function (CDF) by using the error function as a random variable Using PDF of error distribution An ideal pdf for error distributions Read more

By admin, ago