ashutosh nayakTricks of Getting Desired Output from LLMs in RAGs: Purely AnecdotalThere are multiple sources on learning “Prompt Engineering”. This blog is not on “How to prompt” and I assume you already know the basics…·4 min read·Mar 17, 2024----
ashutosh nayakBrief Introduction to Different Types of Prompting for LLMsPrompt engineering aims at designing prompts to get the desired output from LLMs. While prompt engineering is vast, this blog briefly…·9 min read·Mar 17, 2024----
ashutosh nayakDecision Tree: What is Information Gain Criteria to Split NodesThis is a short blog on understanding the idea behind splitting a tree at a node (into two sub-branches). One of the criteria is…·2 min read·Aug 19, 2022----
ashutosh nayakinTowards Data Sciencep-Value and Power of a TestIdea of p-Value·3 min read·Sep 1, 2020--1--1
ashutosh nayakinTowards Data ScienceAkaike Information CriteriaThe idea behind AIC·3 min read·Mar 13, 2020----
ashutosh nayakinTowards Data ScienceIdea Behind LIME and SHAPIntuition behind ML interpretation models·7 min read·Dec 22, 2019--2--2
ashutosh nayakinTowards Data ScienceXGBoost: An Intuitive ExplanationHow XGBoost trees are constructed·3 min read·Dec 17, 2019----
ashutosh nayakinTowards Data ScienceCross-entropy: From an Information theory point of viewConnecting Information theory with cross-entropy loss function·4 min read·Jun 22, 2019----
ashutosh nayakinTowards Data ScienceDealing with Type II EndogeneityExamples from the literature dealing with Type II endogeneity·3 min read·Jun 2, 2019----
ashutosh nayakinTowards Data ScienceDealing with Type I endogeneityUsing ice-cream vendor example to explain Type I endogeneity·2 min read·Jun 2, 2019----