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  1. Dictionary
    robust
    /rə(ʊ)ˈbʌst/

    adjective

    More definitions, origin and scrabble points

  2. 23 jam yang lalu · Definitions of robustly. adverb. in a robust manner. “he was robustly built” Cite this entry. Style: MLA. "Robustly." Vocabulary.com Dictionary, Vocabulary.com, https://www.vocabulary.com/dictionary/robustly. Accessed 18 Aug. 2024. Copy citation. Try a free round of VocabTrainer, vocabulary learning that adapts to you!

  3. 1 hari yang lalu · 1 Define Robustness. Robustness in control engineering refers to a system's ability to maintain performance despite uncertainties and variations in its environment or model. This includes ...

  4. 1 hari yang lalu · In this comprehensive guide, we will delve into the intricacies of building robust APIs, an essential skill for any data engineer or developer. You will gain practical insights into the art and science of crafting APIs that not only enable data flow but do so with reliability, efficiency, and scalability in mind.

  5. 1 Ogo 2024 · Organizations must define robust policies and arm themselves with relevant statistics to navigate the complexities of product quality with confidence. Establishing precise guidelines and embracing advanced tools for automated testing are fundamental steps toward achieving the consistency required in today’s dynamic technology landscape.

  6. 23 Jul 2024 · The concept of robustness was first used in the biology and engineering fields to define the ability of a system to put in place prompt actions that are able to deal with a shock in ways that uphold the main functions of the system.

  7. 7 Ogo 2024 · Digital transformation is the fundamental rewiring of how an organization operates. The goal of a digital transformation, as outlined in the new McKinsey book Rewired: A McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 20, 2023), should be to build a competitive advantage by continuously deploying tech at scale to ...

  8. 6 hari yang lalu · I am currently developing a statistical package in Python using R code as a reference, and I've noticed different results between the two programs when calculating a robust covariance matrix in Python versus R. Using the following code with equivalent input data ( x) in Python: # dummy data. x = np.array([.

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