7+ Resume Power Verbs to Replace "Supported"

another word for supported on resume

7+ Resume Power Verbs to Replace "Supported"

When describing contributions to a crew or mission on a resume, utilizing diversified and impactful language is essential. As a substitute of counting on the widespread time period “supported,” think about stronger motion verbs that spotlight particular accomplishments. For example, as a substitute of stating “Supported the advertising and marketing crew,” one may write “Facilitated advertising and marketing campaigns” or “Contributed to advertising and marketing technique improvement.” This demonstrates a extra lively function and gives a clearer image of the candidate’s involvement.

Selecting exact verbs strengthens a resume by showcasing quantifiable achievements and demonstrating the impression of 1’s work. This degree of element helps potential employers perceive the worth a candidate brings and differentiates them from different candidates. Traditionally, resumes have advanced from easy lists of earlier employment to dynamic paperwork highlighting abilities and accomplishments. This shift necessitates a extra nuanced vocabulary, transferring past generic phrases like “supported” to showcase a candidate’s distinctive contributions.

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Fix: 0d/1d Target Tensor Expected, Multi-Target Not Supported Error

0d or 1d target tensor expected multi-target not supported

Fix: 0d/1d Target Tensor Expected, Multi-Target Not Supported Error

This error sometimes arises inside machine studying frameworks when the form of the goal variable (the info the mannequin is making an attempt to foretell) is incompatible with the mannequin’s anticipated enter. Fashions typically anticipate a goal variable represented as a single column of values (1-dimensional) or a single worth per pattern (0-dimensional). Offering a goal with a number of columns or dimensions (multi-target) signifies an issue in information preparation or mannequin configuration, resulting in this error message. As an illustration, a mannequin designed to foretell a single numerical worth (like worth) can not instantly deal with a number of goal values (like worth, location, and situation) concurrently.

Accurately shaping the goal variable is prime for profitable mannequin coaching. This ensures compatibility between the info and the algorithm’s inside workings, stopping errors and permitting for environment friendly studying. The anticipated goal form often displays the precise process a mannequin is designed to carry out. Regression fashions often require 1-dimensional or 0-dimensional targets, whereas some specialised fashions would possibly deal with multi-dimensional targets for duties like multi-label classification. Historic improvement of machine studying libraries has more and more emphasised clear error messages to information customers in resolving information inconsistencies.

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