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| --- | ||
| layout: post | ||
| title: Python Neural Net | ||
| date: April 4, 2018 | ||
| categories: projects | ||
| description: A basic neural net that recognizes binary patterns | ||
| author: Nick Vaccarello | ||
| author-image: https://avatars1.githubusercontent.com/u/32079670?s=400&u=9056d77d53eb2a07e828fcb70adf0893bbce8f21&v=4 | ||
| author-bio: First year Computer Science major, proud memeber of CSH. | ||
| author-email: nickwvac@gmail.com | ||
| author-social: | ||
| github: https://github.com/nickvac642 | ||
| --- | ||
| When I first had the idea, I came to the consensus to create it from scratch to the best of my ability. This did involve | ||
| some tutorial work and research on how nerual nets actually worked inculding the complicated mathmatics behind them. I | ||
| started my initial research during the winter break with what a neural net is and how to train them. I researched | ||
| algorithms, such as back-propagation and methods to reduce the cost function such as Gradient Decent and Stochastic | ||
| Gradient Decent. | ||
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| My neural net utilizes Stochastic Gradient Decent which is a varient of Gradient decent that takes mini batches of my | ||
| training data and decides the steps to take to minimize the cost function quickly but less precise than Gradient Decent, | ||
| but with the size and extent of my neural net that sacrifice is justified. | ||
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| All Methods aside, my neural net in its current state, has the ability to recognize a binary pattern of the users choosing. | ||
| An example of one would be a list of binary inputs that map to either a 0 or 1 depending on the location of the 0s or 1s | ||
| or even the number of one present in the data, what ever pattern it can train with it and has an average accuracy of about | ||
| 97%. | ||
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| Some difficulties I had along the way included understanding the math, which I still do not know to its fullest extent, and | ||
| figuring out how to back-propagate my error. It took me about two months after research to have a neural net that | ||
| implements all the neccessary algorithms to effectively train against my data. It wasn't until recently that I carried out | ||
| the ability for user input and testing. | ||
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| Overall I loved learning about how neural nets tick and I have a plethera of paths I want to take with this project | ||
| such as refactoring it into a Convolutional neural net, having the user enter in their own training data and expected | ||
| value and or reading data in from an outside source. | ||
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Fix date format to
2018-04-12