DLDA: Complete Implementation with Training and Recognition Processes

This is my independently developed DLDA (Deep Linear Discriminant Analysis) model for my graduation project, which implements a deep learning-based framework for both training and recognition tasks. During the training phase, the model efficiently processes large datasets using batch optimization techniques and implements fast convergence algorithms. The recognition module employs sophisticated feature extraction and classification mechanisms to accurately identify targets. The architecture incorporates multiple hidden layers with activation functions like ReLU, and utilizes backpropagation with gradient descent optimization to achieve high-performance metrics. Through careful design iterations and practical implementation testing, the model ensures robust performance and reliability. This graduation project has provided me with comprehensive knowledge about deep learning architectures and pattern recognition methodologies, significantly deepening my understanding of this field.