Reading pdf - Deepstash

Reading pdf

  1. browsers: Firefox and Chromium ,
  2. Evince (or Atril on the GNOME 2 fork, MATE ),
  3. SumatraPDF
  4. KDE's Okular
  5. All of these have the ability to complete PDF forms, view and make comments, search for text, select text, and so on.
  6. For a generic, simple, and fast PDF reader, try xpdf .

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MORE IDEAS FROM Open source alternatives to Adobe Acrobat for PDFs

  1. , LibreOffice : export functionality
  2. Scribus , Inkscape , and GIMP all support native PDF export,
  3. CUPS printing system does an excellent job of outputting documents as PDF
  4. Pandoc : shell terminal
  5. several other solutions, including Docbook , Sphinx , and LaTeX .

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  1. LibreOffice Draw , giving you full access to the text and images.
  2. Inkscape , If you don't have a font installed, Inkscape (through the Poppler renderer)
  3. PDFedit ,
  4. pdftk-java (PDF ToolKit) command useful. It can extract and inject bookmark metadata, rearrange and concatenate pages, combine many PDFs into one
  5. PDFSam has many similar functions, but includes a graphical interface.
  6. GhostScript command, gs . : low-level tasks == swapping fonts; adjusting resolution of images; dropping images

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RELATED IDEA

  • Python is a general-purpose, object-oriented programming language.
  • It emphasises code readability by using white space.
  • It is easy to learn.
  • It is a favourite of programmers and developers.
  • Python is very well suited for use in machine learning at a large scale.
  • Its suite of specialised deep learning and machine learning libraries includes tools like scikit-learn, Keras and TensorFlow. It enables data scientists to develop sophisticated data models that plug directly into a production system.

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Big data Hadoop
  • Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT) , that's a key consideration.
  • Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
  • Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
  • Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
  • Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
  • Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.

MapReduce programming is not a good match for all problems. It’s good for simple information requests and problems that can be divided into independent units, but it's not efficient for iterative and interactive analytic tasks. MapReduce is file-intensive. Because the nodes don’t intercommunicate except through sorts and shuffles, iterative algorithms require multiple map-shuffle/sort-reduce phases to complete. This creates multiple files between MapReduce phases and is inefficient for advanced analytic computing.

There’s a widely acknowledged talent gap. It can be difficult to find entry-level programmers who have sufficient Java skills to be productive with MapReduce. That's one reason distribution providers are racing to put relational (SQL) technology on top of Hadoop. It is much easier to find programmers with SQL skills than MapReduce skills. And, Hadoop administration seems part art and part science, requiring low-level knowledge of operating systems, hardware and Hadoop kernel settings.

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Building a serverless framework certainly has its challenges. One of which is the deployment of cloud infrastructure, which, in the world of serverless, is one of the fundamental operations developers need to do, even while the application is still in development.

Before the version 5 release , Webiny relied on an infrastructure provisioning technology called Serverless Components (not to be confused with Serverless Framework ).

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