<commit_before>OCR with Neural Nets
====================

this project is for computing practice & theory at the evergreen state college.

the end goal of this project is to implement optical character recognition for
handwritten characters and to give the unicode equivalents using neural networks.

dependancies: python3

in order to run, clone the repo and navigate to the clone folder in the command line.

there you can change the inputs to the neural network in the *params* subfolder. I named
my params file params.dat, so I run my neural net by typing the following in the command line

```
python3 main.py params.dat
```

Components:
============
* main
  * main
  * hasKey
  * indent
  * todo:
     * need to make prompt and interactivity + visualization
* neuralNet
  * neuron
  * neuralNetLayer
  * neuralNet
  * todo:
     * might need some more tools, seems functional otherwise
* propagate
  * backprop
  * sigmoid
  * derivSigmoid
  * errorGradientOutputLayer
  * errorGradientHiddenLayer
  * derivActivation
  * activation
  * y
  * deltaThreshhold
  * deltaWeight
  * sum
  * todo:
     * test neural nets with hidden layers
* fileLib
  * getDataFromFile
  * todo:
     * seems finished
* imagePreProcessing
  * todo:
     * need to start

the following sources are the inspiration for the code
i've written and the resources that i've found useful.

Sources:
--------
   - http://www.ibm.com/developerworks/library/l-neural/
   - http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html
   - http://www.ai-junkie.com/ann/evolved/nnt1.html
   - http://www.engineering.upm.ro/master-ie/sacpi/mat_did/info068/docum/Neural%20Networks%20for%20Pattern%20Recognition.pdf
   - http://itee.uq.edu.au/~cogs2010/cmc/chapters/BackProp/

<commit_msg>cleaned readme<commit_after>OCR with Neural Nets
====================

this project is for computing practice & theory at the evergreen state college.

the end goal of this project is to implement optical character recognition for
handwritten characters and to give the unicode equivalents using neural networks.

**dependancies**: python3

in order to run, clone the repo and navigate to the clone folder in the command line.

there you can change the inputs to the neural network in the *params* subfolder. I named
my params file params.dat, so I run my neural net by typing the following in the command line

```
python3 main.py params.dat
```

Components:
============
* main
  * main
  * hasKey
  * indent
  * todo:
     * need to make prompt and interactivity + visualization
* neuralNet
  * neuron
  * neuralNetLayer
  * neuralNet
  * todo:
     * might need some more tools, seems functional otherwise
* propagate
  * backprop
  * sigmoid
  * derivSigmoid
  * errorGradientOutputLayer
  * errorGradientHiddenLayer
  * derivActivation
  * activation
  * y
  * deltaThreshhold
  * deltaWeight
  * sum
  * todo:
     * test neural nets with hidden layers
* fileLib
  * getDataFromFile
  * todo:
     * seems finished
* imagePreProcessing
  * todo:
     * need to start

the following sources are the inspiration for the code
i've written and the resources that i've found useful.

Sources:
--------
   - http://www.ibm.com/developerworks/library/l-neural/
   - http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html
   - http://www.ai-junkie.com/ann/evolved/nnt1.html
   - http://www.engineering.upm.ro/master-ie/sacpi/mat_did/info068/docum/Neural%20Networks%20for%20Pattern%20Recognition.pdf
   - http://itee.uq.edu.au/~cogs2010/cmc/chapters/BackProp/


{'fraction_non_alphanumeric': 0.10008529997156668, 'fraction_numerical': 0.010235996588001138, 'mean_word_length': 3.5218508997429305, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 6, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1282244', 'n_tokens_mistral': 1111, 'n_tokens_neox': 1053, 'n_words': 351}