Ontotext

APRIL 6, 2023

More and more companies are using them to improve a variety of tasks from product range specification and risk analysis to supporting self-driving cars. This allows companies to model and optimize the interactions between the various computers that make a car run, ensuring everything is operating in sync to meet the desired specifications.

Manufacturing 83

Manufacturing Cost-Benefit Knowledge Discovery Optimization 83

Smart Data Collective

MARCH 13, 2020

But if you’re still working with outdated methods, you need to look for ways to fully optimize your approach as you move forward. Phase 4: Knowledge Discovery. One way to ensure optimal speed and efficiency is to leverage the correct mix of hardware and software. 5 Tips for Better Data Science Workflows.

Data Science 100

Data Science Key Performance Indicator Knowledge Discovery Visualization 100

Ontotext

MARCH 8, 2023

Here, I will draw upon our own experience from client projects and lessons learned to provide a selection of optimal use cases for knowledge graphs and semantic solutions along with real world examples of their applications. A risk issue in one financial institution could result in a domino effect for many.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Ontotext

APRIL 4, 2019

This is a knowledge that anyone can get, but it would take much longer than optimal. But still, is there a risk that AI could replace people at their workplace? Economy.bg: The pros in this respect are indisputable. How to prepare for a future without employment? Milena Yankova : Will AI replace us?

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

These estimates can be useful to make risk-adjusted decisions and explore-exploit trade-offs, or to find situations where the underlying regression method is particularly good or bad. $mathrm{var}(theta | t, y)$ estimates the accuracy of $E(theta | t, y)$ — this tells us how much we know about each item.

KDD 40

KDD Testing Machine Learning Modeling 40

Domino Data Lab

AUGUST 1, 2021

This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. This is to be expected, as there is no reason for a perfect 50:50 separation of the good vs. bad credit risk. Conference on Knowledge Discovery and Data Mining, pp. 1 570 0 570 Name: credit, dtype: int64.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139