Digital Freedom Foundation is proud to announce the release of version 5.
The much awaited version 5 of this free, fast and feature rich accounting software is more enterprise ready with a lot of new additions to the already existing modules. These new additions include Sales and Purchase order, Credit and Debit note and Rejection Note, all GST ready.
Invoice and Delivery chalan modules are also GST ready. We also have the sales and purchase register revamped for new updates to GST.
After a long discussion with our existing customer base, we decided to export all reports to xlsx instead of the previous ODS format.
We have also revamped the Profit and Loss statement and made it more auditor friendly.
Lastly we have made the import from software like Tally even more easy.
You can now download the new version from GNUKhata website while help desk is ready to take your queries, requests and new feature demands.
In fact the support help desk is the biggest thing we have to offer to our customers from this time around.
Hope you all will like this version of GNUKhata and have more reasons to exclusively use it.
The Founder and Secretary of Digital Freedom Foundation, Mr. Krishnakant Mane had given a seminar on “Free and Open Source Career Opportunities” at St. John College Of Engineering And Management on 8th January, 2017. A team from Digital Freedom Foundation – Krishnakant Mane himself, Prajkta Patkar and Abhijith Balan then conducted a workshop on “Machine Learning using Python” on 2nd and 3rd February, 2018. The workshop began with an introduction to Python programming. Later on data structures in Python, defining functions and concepts of OOP were discussed.
Once everyone became familiar with Python a demo of Emacs text editor was given. Afterwards an introduction to machine learning and its applications was given. Python libraries that were to be used in the exercises and their installation was discussed. The attendees were then walked through the steps involved in a machine learning project through exercises on supervised machine learning. The workshop concluded with a discussion on various sources for gaining further know how on machine learning and Python.