Hyperbolic Learning Rate Scheduler
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Updated
Jun 12, 2026 - 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
The Newton-like learning rate scheduler
Lightweight CNN for 28×28 grayscale multi-class image classification with augmentation & regularization.
PyTorch implementation of the generalized Newton's method for learning rate selection
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.
🖼️A PyTorch-based Convolutional Neural Network (CNN) built from scratch to classify CIFAR-10 images. Achieves 82.93% test accuracy using Batch Normalization, Dropout, Data Augmentation, and a ReduceLROnPlateau scheduler. Includes detailed evaluation curves, confusion matrices, and per-class analyses.
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