Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Additionally, this will enable an organization to utilize resources optimally and enhance the customer’s experience. Data Mining Models. Data Mining Process. Classification.

Data mining 77

Data mining KDD Data Science Forecasting 77

Ontotext

APRIL 4, 2019

Milena Yankova : We help the BBC and the Financial Times to model the knowledge available in various documents so they can manage it. This is a knowledge that anyone can get, but it would take much longer than optimal. Economy.bg: You work with media companies such as the BBC and the Financial Times.

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

The Unofficial Google Data Science Blog

JULY 22, 2020

In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a. These MAB algorithms are great at maximizing reward when the models are perfectly specified and probabilities are accurate.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Ontotext

JULY 29, 2021

One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. We rather see it as a new paradigm that is revolutionizing enterprise data integration and knowledge discovery. We can’t imagine looking at the Semantic Web as an artifact.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.

KDD 40

KDD Testing Machine Learning Modeling 40

Ontotext

FEBRUARY 21, 2024

RAG and Ontotext offerings: a perfect synergy RAG is an approach for enhancing an existing large language model (LLM) with external information provided as part of the input prompt, or grounding context. So we have built a dataset using schema.org to model and structure this content into a knowledge graph.

Knowledge Discovery 64

Knowledge Discovery Technology Metadata Data-driven 64

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Ontotext

AUGUST 31, 2023

The growth of large language models drives a need for trusted information and capturing machine-interpretable knowledge, requiring businesses to recognize the difference between a semantic knowledge graph and one that isn’t—if they want to leverage emerging AI technologies and maintain a competitive edge.

Modeling 64

Modeling Optimization Knowledge Discovery Enterprise 64