Repository containing models based on ideas of Machine learning and Deep learning. List of files:

1. Simple Sequential Model

   • Uses randomly generated trainin set (10% of which is used in validation set) and test data
   • Shows final predictions in a confusion matrix

2. Cat and Dog Classifier - Convolution Neural Network

   • Uses a data set of 1300 images (1000 for training set, 200 for validation set, 100 for test set) randomly picked out of a larger data set of 25000 images
   • Image Data: https://www.kaggle.com/c/dogs-vs-cats/data (25000 images of cats and dogs)
   • Model experiences overfitting and needs to be improved
   • Model has not been tested for now due to overfitting on the training set

3. Cat and Dog Classifier 2.0 [using existing model] - Convolution Neural Network

   • Trains existing model VGG16 (with some alterations)
   • Uses data prepeartion used in the previous upload (Cat and Dog Classifier - Convolution Neural Network)
   • Highly accurate model with no overfitting

4. Image Classification [using existing model] - MobileNet

   • Importing a pre-trained model and testing its ability of identify sample images
   • This model is broader than the Cat and Dog Classifiers previously uploaded
   • It tells percentage of possible assumptions of an object present in an image provided to it

5. Sign Language Digits Classification [using fine tuned existing model] - MobileNet

   • Uses dataset https://github.com/ardamavi/Sign-Language-Digits-Dataset
   • Makes use of pre-existing model

6. Data Augmentation

   • Creates data from a single image to be processed by a neural network
   • Image is rotated, flipped, shifted e.t.c to produce a set of more images

7. First Neural Network with Keras API

   • Uses randomly generated dataset (90% test data, 10% validation data)
   • View blog post: https://dev.to/muizalvi/build-your-first-neural-network-with-the-keras-api-35b4
   • Shows final predictions in a confusion matrix