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User:Sudeepam: Difference between revisions
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== Project Description == | == Project Description == | ||
So my special focus is to have a minimal trade-off between the speed and accuracy of the feature. Before talking about that, let me first describe the three kinds of Neural Networks (Depending on the training data available) that we can end up making. | |||
:'''1) A network trained with only the correct spellings of the inbuilt functions''' | :'''1) A network trained with only the correct spellings of the inbuilt functions''' | ||
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:'''2) A network trained with the correct spellings of the functions and self created errors''' | :'''2) A network trained with the correct spellings of the functions and self created errors''' | ||
This would be slightly harder to make but should give us an improved performance. I will '''create some misspellings''' for all the functions, by additional inclusion, deletion, substitution, and transportation of one or two letters and then add all these self created misspellings to the data-set which will be used to train the network. Such a network would understand what '''correct spellings and random typographic errors''' look like. It will easily understand substitutions and transportations like the previous network but should also be more accurate while predicting errors caused due to additions/deletions. However, it is worth mentioning here that ''we may create errors while creating errors.'' Because our training data will be randomly modified for this network, although the chances are rare, the Neural Network may show uncertain | This would be slightly harder to make but should give us an improved performance. I will '''create some misspellings''' for all the functions, by additional inclusion, deletion, substitution, and transportation of one or two letters and then add all these self created misspellings to the data-set which will be used to train the network. Such a network would understand what '''correct spellings and random typographic errors''' look like. It will easily understand substitutions and transportations like the previous network but should also be more accurate while predicting errors caused due to additions/deletions. However, it is worth mentioning here that ''we may create errors while creating errors.'' Because our training data will be randomly modified for this network, although the chances are rare, the Neural Network may show uncertain behaviour. | ||
:'''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''' | ||