Modeling Statistics Technology 
Analytics Vidhya
JANUARY 24, 2023
Introduction Statistical models are significant for understanding and predicting complex data. A viable area for statistical modeling is time-series analysis. Time series data are collected over time and can be found in various fields such as finance, economics, and technology.
O'Reilly on Data
NOVEMBER 19, 2024
Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. See the primary sources “ REALM: Retrieval-Augmented Language Model Pre-Training ” by Kelvin Guu, et al., at Facebook—both from 2020. What is GraphRAG?
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David Menninger's Analyst Perspectives
JANUARY 15, 2025
I use the term external data to include any information about the world outside an organization (including economic and market statistics), competitors (such as pricing and locations) and customers. This provides useful information about what to do next time to achieve a better outcome and how to refine the model to improve its accuracy.
O'Reilly on Data
MAY 21, 2019
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.
Analytics Vidhya
SEPTEMBER 17, 2022
Introduction Conventionally, an automatic speech recognition (ASR) system leverages a single statistical language model to rectify ambiguities, regardless of context. This article was published as a part of the Data Science Blogathon. However, we can improve the system’s accuracy by leveraging contextual information.
O'Reilly on Data
DECEMBER 12, 2019
Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]
datapine
NOVEMBER 19, 2019
In this post, we’re going to give you the 10 IT & technology buzzwords you won’t be able to avoid in 2020 so that you can stay poised to take advantage of market opportunities and new conversations alike. Exclusive Bonus Content: Download our Top 10 Technology Buzzwords!
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