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learning-rate-scheduler

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This project modifies the classic VGG16 architecture to classify images into four distinct categories with high accuracy. It incorporates data augmentation, dynamic learning rate adjustments, and comprehensive performance evaluation using accuracy metrics and confusion matrices. Built with PyTorch and supported by a suite of powerful libraries

  • Updated Dec 11, 2023
  • Python

An intermediate-level deep learning project that compares Convolutional Neural Networks (CNN) and Multi-Layer Perceptrons (MLP) on the MNIST handwritten digits dataset. This project demonstrates data augmentation, learning rate scheduling, and visual comparison of model performance

  • Updated Oct 4, 2025
  • Jupyter Notebook

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