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 NeuralNetwork class 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_split from 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.