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