Built an end-to-end, object-oriented neural network in pure Python and NumPy, achieving over 80% accuracy on the MNIST dataset for handwritten digit classification. Implemented numerically stable activations, He initialization, and momentum-based gradient descent. Optimized hyperparameters, enabled flexible multilayer architectures, and applied robust preprocessing and data imputation to improve training efficiency.
