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How difficult would it be to make a neural network create an Excel sheet out of text in PDF format, if the text is structured to different lines/parts the same way it would be in different columns in Excel?
There is a humorous paper relevant to your question called Deep Spreadsheets with Excelnet. It proposes the ridiculous idea of What You See Is What You Get (WYSIWYG) editing of weights and notes the “synergy” and “enterprise-readiness” of doing things this way. The abstract reads like this. As anyone in machine learning and computer vision will tell you, Deep Learning is the right tool to solve the problem. And as anyone in business and finance will tell you, Excel is the right platform to implement your solution. But until now, there has been no way to do Deep Learning in Excel. To fill this gap we have developed EXCELNET , the ultimate synergy of spreadsheets and Deep Neural Networks. This joke sheds light on your question for a few reasons. Spreadsheets are good for exploratory, computationally-quick calculations (usually run on the CPU in your PC) on simple data that can fit into rows and columns. Neural networks are computationally expensive tools (often run in batch mode on specialized hardware like TPUs/GPUs) that usually work on complicated data like videos that are not well-suited to a spreadsheet representation. Not to mention the software stack you would use to begin writing excel formulas for neural networks is underwhelming; c++ (which is used to implement tensorflow)has libraries like CUDNN, CUBLAS, and eigen. Are there good alternatives to this in the excel language? However, while it is humorous to propose an excel-based deep learning framework, it may be reasonable to interface your spreadsheets with neural networks that are implemented externally. For this there are tools like the pandas library which allows python programs (which can interface nicely with your neural network library of choice) to read and write excel data.
PDF documents can be cumbersome to edit, especially when you need to change the text or sign a form. However, working with PDFs is made beyond-easy and highly productive with the right tool.
How to Create PDF with minimal effort on your side:
- Add the document you want to edit — choose any convenient way to do so.
- Type, replace, or delete text anywhere in your PDF.
- Improve your text’s clarity by annotating it: add sticky notes, comments, or text blogs; black out or highlight the text.
- Add fillable fields (name, date, signature, formulas, etc.) to collect information or signatures from the receiving parties quickly.
- Assign each field to a specific recipient and set the filling order as you Create PDF.
- Prevent third parties from claiming credit for your document by adding a watermark.
- Password-protect your PDF with sensitive information.
- Notarize documents online or submit your reports.
- Save the completed document in any format you need.
The solution offers a vast space for experiments. Give it a try now and see for yourself. Create PDF with ease and take advantage of the whole suite of editing features.
Create PDF: All You Need to Know
The good news is that the pandas project has an Excel backend, and it's only 5. If you want a deep learning tool that's flexible, fast, has a well-developed math engine, and is open source to use it with the pandas' library, consider using an Excel-based neural network framework for your next deep learning project. Exxon, Microsoft, Gizmo, Spark and TensorFlow are all great Deep Learning platforms.