Implements https://arxiv.org/abs/1711.05101 AdamW optimizer, cosine learning rate scheduler and "Cyclical Learning Rates for Training Neural Networks" https://arxiv.org/abs/1506.01186 for PyTorch framework

Implemented GPT from scratch

Prepare-Training-Model-Yolo11

ECE-666: Applied Optimization methods for Machine Learning. This repository contains PyTorch implementations for a variety of recent optimization algorithms in deep learning.

Lightweight SegFormer3D-inspired 3D Transformer for right-ventricle segmentation in cardiac MRI (evaluated on the public M&Ms-2 multi-center, multi-view dataset).

The goal of this project is to devise an accurate CNN-based classifier able to distinguish between Cat and Dog in images where the animal is predominant.

Deep Neural Network built from scratch to tackle ML binary classification predicting Titanic survival (Kaggle Competition). Best DNN AdamW 77.03% and best RF 78.95%.

Cat pain recognition assistance based on ResNet18 with AdamW and focal loss

ResNet-18 to classify MRI images into four categories: glioma, meningioma, pituitary, and no tumor.

This repository demonstrates and compares the behavior of four widely used optimization algorithms in machine learning: Momentum (SGD with Momentum) RMSProp Adam AdamW The goal of this project is to visualize how these optimizers update parameters while minimizing a simple objective function.

Multitask BERT fine-tuning for sentiment analysis, paraphrase detection, and semantic similarity (SST, QQP, STS-B). Explores SMART regularization, Sophia optimizer, and gradient surgery. ~5% robustness gain over vanilla AdamW baseline.