Ramin Mehran’s Post

What is Retrieval Augmented Generation and why is the data you feed it so important? http://spkl.io/6041fVMXS Read on to understand the importance of the information retrieval steps in such a workflow and how vector and ontology-based methods compare in our blog by Joseph Mullen, Director of Data Science & Professional Service at SciBite. http://spkl.io/6041fVMXS #LargeLanguageModels #LLMs #Ontologies

#RAG has fundamental limitations that prevent it from generating accurate answers for diverse queries on complex documents or on datasets at scale. Mehul Shah writes about the need for a new approach: LLM-Powered Unstructured Analytics. We call it LUnA, and this architecture is at the core of #Sycamore. Read about LUnA here: https://lnkd.in/gThndXVf #Sycamore #Aryn #Analytics #UnstructuredData #Search #GenAI

What is Retrieval Augmented Generation and why is the data you feed it so important? http://spkl.io/60484CS7A Read on to understand the importance of the information retrieval steps in such a workflow and how vector and ontology-based methods compare in our latest blog by Joseph Mullen Joe Mullen, Director of Data Science & Professional Service at SciBite. http://spkl.io/60484CS7A #LargeLanguageModels #LLMs #Ontologies

📚 Dive into "Exploring Summarization Techniques with LLMs" in our Data Mastery Series! Discover how to leverage Large Language Models (LLMs) to generate concise and insightful summaries from extensive texts. This episode covers various advanced summarization methods, including Map-Reduce, Custom Prompts, and Cluster-Based Summarization. Perfect for anyone looking to enhance the efficiency of information processing and ensure critical details are captured effectively. Join us for an insightful journey into the world of LLM summarization techniques!

Always remember folks: data rarely fits our core assumptions in regression analysis. This is why you should always make sure to run your diagnostic tests and provide as much error specification to your model as needed. *PS, if your data somehow satisfies all assumptions of regression analysis, then chances are you’ve engaged in data torture (big no-no). #statistics #regression #datascience

Causal Validation: A Unified Theory of Everything How to detect and fix any type of error in a directed acyclic graph so that it is a valid representation of the underlying data Graham Harrison Towards Data Science Graham Harrison · Following Published in Towards Data Science · 28 min read · 7 hours ago 145 1 Photo by Guillermo Ferla on Unsplash Introduction Causal inference is an emerging field within machine learning that can move beyond predicting what could happen to explaining why it will happen and it doing so offers the promise of permanently resolving the underlying problem rather than dealing with the potential fallout. One of the key components of a causal model is a “Directed Acyclic Graph” (DAG) which captures the cause-and-effect relationships between variables and events in a simple visual format but the main issue with DAGs is that they are typically constructed subjectively by the domain experts. Hence there is no guarantee that the DAG will be correct and if it is incorrect the calculations and conclusions of a causal inference model are likely to be wrong. Causal Validation is the term used to describe the process of checking a DAG against the underlying data it represents with the objective of identifying any mistakes or inconsistencies and fixing them and if this can be done reliably it will ensure that the conclusions of causal inference and the associated… https://lnkd.in/dRxJ67WW

Causal Validation: A Unified Theory of Everything How to detect and fix any type of error in a directed acyclic graph so that it is a valid representation of the underlying data Graham Harrison Towards Data Science Graham Harrison · Following Published in Towards Data Science · 28 min read · 7 hours ago 145 1 Photo by Guillermo Ferla on Unsplash Introduction Causal inference is an emerging field within machine learning that can move beyond predicting what could happen to explaining why it will happen and it doing so offers the promise of permanently resolving the underlying problem rather than dealing with the potential fallout. One of the key components of a causal model is a “Directed Acyclic Graph” (DAG) which captures the cause-and-effect relationships between variables and events in a simple visual format but the main issue with DAGs is that they are typically constructed subjectively by the domain experts. Hence there is no guarantee that the DAG will be correct and if it is incorrect the calculations and conclusions of a causal inference model are likely to be wrong. Causal Validation is the term used to describe the process of checking a DAG against the underlying data it represents with the objective of identifying any mistakes or inconsistencies and fixing them and if this can be done reliably it will ensure that the conclusions of causal inference and the associated… https://lnkd.in/d76ASeNm

👉🏼 Leveraging Open-Source Large Language Models for Data Augmentation in Hospital Staff Surveys: Mixed Methods Study 🤓 Carl Ehrett 👇🏻 https://lnkd.in/e86rvfKq 🔍 Focus on data insights: - 📊 The study identifies that the best-performing generative model for data augmentation is LLaMA 7B, utilized at temperature 0.7 with 100 synthetic augmentations. - ✅ Robustly Optimized BERT Pretraining Approach

Do you remember learning about the homoscedastic (what a word!) errors assumption for linear regression in stats 101? I recently wrote an article about how and why that assumption can be loosened by using heteroscedastic robust errors on Towards Data Science! If heteroscedasticity is keeping you up at night you should check it out!

Random Data Science pro-tip. Stop using one-hot encoding for high cardinality categorical data, in which each category can only be present once per row of data. If you're careful about splitting your train-test-validation sets, Target Encoding provides a much more straightforward way of encoding categorical data without blowing up the size of your data frame.