slides code

DSCEWebinar

this repository has the distribution code to Deeplearning Webinar

1. Banknotes

This is an example of a Artificial Neural Network where you train a neural network to differentiate between originial and counterfeit banknotes.

Contents

1. banknotes.csv (file containing our dataset)
2. banknotes.py (the python script which generates and tests the model)
3. requirements.txt

   Instructions to run the program

4. make a virtual environment (assuming that you have already installed python on your system) by running this command in your terminal.

   python -m venv venv
   

5. Activate your virtual environment by running this comand in your terminal

   source venv/Scripts/activate
   

6. Install the required packages by running this command in your terminal.

   pip3 install -r requirements.txt
   

7. run the program by executing this command in your terminal.

   python banknotes.py
   

   # 2. Convolution This is a program which takes .png format picture and returns a convoluted image. ## Contents

8. convolution.py (this file has the program which convolutes the image)
9. bridge.png (this file has a picture to test the program)
10. requirements.txt

Instructions to run the program

1. make a virtual environment (assuming that you have already installed python on your system) by running this command in your terminal.

   python -m venv venv
   

2. Activate your virtual environment by running this comand in your terminal

   source venv/Scripts/activate
   

3. Install the required packages by running this command in your terminal.

   pip3 install -r requirements.txt
   

4. run the program by executing this command in your terminal.(you can convolute any other png image you like by saving the image in same directory as convolution.py and entering the name of the image instead of bridge.png in your terminal)

   python convolution.py bridge.png
   

   3. Numbers

   This is a classic example of a Convolutional Neural Network (CNN) wherein we predict the number drawn on the screen .

   Contents

5. /assets (this directory has the fonts required for the pygame interface)
6. handwriting.py (This is a python script which generates a model)
7. recognition..py (This is a python script which generates a pygame GUI it takes a model as a command line argument based on which it predicts the number drawn)
8. model.h5 (This is a model which can be used to evalute the number drawn, you can pass this as a command line argument in recognition.py)
9. requirements.txt

   Instructions to run the program

10. make a virtual environment (assuming that you have already installed python on your system) by running this command in your terminal.

    python -m venv venv
    

11. Activate your virtual environment by running this comand in your terminal

    source venv/Scripts/activate
    

12. Install the required packages by running this command in your terminal.

    pip3 install -r requirements.txt
    

13. you can create a model (This repository has a ready model which can be used instead) by running this command

    python handwriting.py (filename)
    

    where you are expected to enter name of the model you want to create (file name must end with .h5) Note: this might take sometime

14. run the recognition script to get a GUI .

    python recognition.py model.h5
    

    if you have followed step four and created a model enter the name of that model instead of model.h5

    Contributors

    Ataago , Mashhood