Digit classification plays a crucial role in various applications, from optical character recognition to automated document processing. In this repository, I will outline three different programs that implement digit classification using different techniques and libraries. This aim of this README is to provide a overview of the technical highlights of each program.
- Implements a neural network for digit classification using only the NumPy library.
- Defines a
NeuralNetworkclass with methods for forward and backward propagation. - Utilises activation functions like ReLU and softmax.
- Implements gradient descent for parameter updates.
- Includes utility methods for data preprocessing, evaluation, and accuracy computation.
- Utilises the scikit-image library's HOG feature extraction for digit representation.
- Performs feature scaling using
preprocessing.MaxAbsScaler. - Splits the data into training and testing sets using
train_test_splitfrom scikit-learn. - Implements a neural network with ReLU activation and softmax for multi-class classification.
- Uses gradient descent for parameter updates and evaluates accuracy during training.
- Applies feature scaling using StandardScaler from scikit-learn.
- Compiles the model with the Adam optimiser and sparse categorical cross-entropy loss.
- Trains the model using the training data for a specified number of epochs and batch size.
- Implements a neural network using the Keras library.
- Compiles the model with the Adam optimiser and sparse categorical cross-entropy loss.
Exploring Minimum Redundancy Maximum Relevance (mRMR) feature selection:
- mRMR is an algorithm that selects informative features in classification tasks.
- It chooses features that are highly relevant to the target variable and have low redundancy with each other.
- By maximising relevance and minimising redundancy it is able to form a subset of key features.
- Using this method means that it would potentially select more relevant and non-redundant features from a high-dimensional input.
- This would further enhance the efficiency, performance, and how the NN interprets the data.