Implementation of a 3 layer neural net in numpy, trained and tested on MNIST dataset
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Updated
Oct 21, 2018 - Jupyter Notebook
Implementation of a 3 layer neural net in numpy, trained and tested on MNIST dataset
Advance Machine Learning (CSL 712) Course Lab Assignments
Reproducing the paper "PADAM: Closing The Generalization Gap of Adaptive Gradient Methods In Training Deep Neural Networks" for the ICLR 2019 Reproducibility Challenge
Simple MATLAB toolbox for deep learning network: Version 1.0.3
Implement different variants of gradient descent in python using numpy
A compressed adaptive optimizer for training large-scale deep learning models using PyTorch
Deep Learning Optimizers
Week 1 assignment form Coursera's "Advanced Machine Learning - Introduction to Deep Learning"
Using different optimizers for a comparison study, finding the root of differences by visualization and to find the best case for a specific task
Implementation and comparison of SGD, SGD with momentum, RMSProp and AMSGrad optimizers on the Image classification task using MNIST dataset
CNeuron is a simple singular neural network neuron implementation in C, designed for easy integration into C projects.
flexible and extensible implementation of a multithreaded feedforward neural network in Java including popular optimizers, wrapped up in a console user interface
Effect of Optimizer Selection and Hyperparameter Tuning on Training Efficiency and LLM Performance
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