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  2. This $39 AI-powered résumé app could help outsmart ... - AOL

    www.aol.com/39-ai-powered-r-sum-090000622.html

    TL;DR: As of April 6, get a lifetime subscription to this AI Résumé Builder for just $39 — a 97% discount.You may be the most qualified candidate for a job with a résumé that proves it, but ...

  3. List of PDF software - Wikipedia

    en.wikipedia.org/wiki/List_of_PDF_software

    As with Adobe Acrobat, Nitro PDF Pro's reader is free; but unlike Adobe's free reader, Nitro's free reader allows PDF creation (via a virtual printer driver, or by specifying a filename in the reader's interface, or by drag-'n-drop of a file to Nitro PDF Reader's Windows desktop icon); Ghostscript not needed. PagePlus: Proprietary: No

  4. Vermont launches app using AI to help create resumes and ...

    www.aol.com/vermont-launches-app-using-ai...

    An application just launched to help Vermonters find and apply for jobs using artificial intelligence technology. Vermont Employment Pathfinder will help best match users with career opportunities ...

  5. Résumé - Wikipedia

    en.wikipedia.org/wiki/Résumé

    A résumé or resume (or alternatively resumé), [a] [1] is a document created and used by a person to present their background, skills, and accomplishments. Résumés can be used for a variety of reasons, but most often are used to secure new jobs, whether in the same organization or another.

  6. PDF - Wikipedia

    en.wikipedia.org/wiki/PDF

    There are many PDF print drivers for Microsoft Windows, the pdfTeX typesetting system, the DocBook PDF tools, applications developed around Ghostscript and Adobe Acrobat itself as well as Adobe InDesign, Adobe FrameMaker, Adobe Illustrator, Adobe Photoshop, that allow a "PDF printer" to be set up, which when selected sends output to a PDF file ...

  7. SqueezeNet - Wikipedia

    en.wikipedia.org/wiki/SqueezeNet

    Model compression (e.g. quantization and pruning of model parameters) can be applied to a deep neural network after it has been trained. [19] In the SqueezeNet paper, the authors demonstrated that a model compression technique called Deep Compression can be applied to SqueezeNet to further reduce the size of the parameter file from 5 MB to 500 ...

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