Hyperbolic Learning Rate Scheduler
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Updated
May 3, 2025 - Python
Hyperbolic Learning Rate Scheduler
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
High-performance PyTorch LR schedulers with cosine annealing, flexible waypoints, plateau steps, and LR scaling. Unified API with pre-computed segments for zero runtime overhead.
Lightweight CNN for 28×28 grayscale multi-class image classification with augmentation & regularization.
The Newton-like learning rate scheduler
PyTorch implementation of the generalized Newton's method for learning rate selection
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
🧠 Compare CNN and MLP models for handwritten digit recognition on the MNIST dataset, enhancing your skills in image classification and model optimization.
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