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User:Sudeepam: Difference between revisions
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:'''3) A network trained with the correct spellings of the functions and the most common typographic errors''' | :'''3) A network trained with the correct spellings of the functions and the most common typographic errors''' | ||
To make this kind of Neural Network, we need to know what common typographic errors look like. With that goal in mind, I have already contacted the people behind octave-online.net [https://octave-online.net/] who say that they are happy to support the development of GNU Octave and have shared a list of top 1000 misspellings with me through email. However the users of octave-online.net are only, one of the parts of the entire user group. '''For best results''', we would require the involvement of the entire Octave community, which, also implies that it will be the hardest and the most fun Neural Network to make. | To make this kind of Neural Network, we need to know what common typographic errors look like. With that goal in mind, I have already contacted the people behind octave-online.net [https://octave-online.net/] who say that they are happy to support the development of GNU Octave and as of now (25th March), have shared a list of top 1000 misspellings with me through email. However the users of octave-online.net are only, one of the parts of the entire user group. '''For best results''', we would require the involvement of the entire Octave community, which, also implies that it will be the hardest and the most fun Neural Network to make. | ||
By creating a script that would be able to catch typographic errors and by asking the users of GNU Octave to use this script and share the most common spelling errors with us, and training the network on the data-set thus created, we’ll create a Neural Network which would understand what '''correct spellings and the most common typographic errors''' look like. Such a network would give good results, almost every-time and with all kinds of errors. This is because when our network knows what common errors are like, most of the times it would '''know the answer''' beforehand. For the remaining times, the network would be able to '''predict the correct answer'''. | By creating a script that would be able to catch typographic errors and by asking the users of GNU Octave to use this script and share the most common spelling errors with us, and training the network on the data-set thus created, we’ll create a Neural Network which would understand what '''correct spellings and the most common typographic errors''' look like. Such a network would give good results, almost every-time and with all kinds of errors. This is because when our network knows what common errors are like, most of the times it would '''know the answer''' beforehand. For the remaining times, the network would be able to '''predict the correct answer'''. | ||