sgd-momentum

Reproducing the paper "PADAM: Closing The Generalization Gap of Adaptive Gradient Methods In Training Deep Neural Networks" for the ICLR 2019 Reproducibility Challenge

A compressed adaptive optimizer for training large-scale deep learning models using PyTorch

Simple MATLAB toolbox for deep learning network: Version 1.0.3

Implement different variants of gradient descent in python using numpy

Effect of Optimizer Selection and Hyperparameter Tuning on Training Efficiency and LLM Performance

Implementation and comparison of SGD, SGD with momentum, RMSProp and AMSGrad optimizers on the Image classification task using MNIST dataset

Deep Learning Optimizers

Week 1 assignment form Coursera's "Advanced Machine Learning - Introduction to Deep Learning"

This project was developed as part of the Biologically Inspired Artificial Intelligence course at the Silesian University of Technology. The goal is to automatically classify eye diseases (diabetic retinopathy, cataract, glaucoma, normal) from retinal images using a convolutional neural network (CNN) inspired by the human visual cortex.

Implementation of a 3 layer neural net in numpy, trained and tested on MNIST dataset

Using different optimizers for a comparison study, finding the root of differences by visualization and to find the best case for a specific task

Deterministic 26-rule verifier for neural-network training steps. Re-derives gradients + optimizer state from named factors; emits canonical JSONL. SGD/Adam/AdamW/SGD-momentum (+coupled-L2 weight decay); live PyTorch + JAX helpers; CPU-only.

Advance Machine Learning (CSL 712) Course Lab Assignments

Hand sign language recognition based on image

A trust-region framework for moment estimation