AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights (ICLR 2021)

Training the fully connected neural network (FCNN) using different optimizers for the backpropagation algorithm and compare the number of epochs that it takes for convergence along with their classification performance. Also building an autoencoder to obtain the hidden representation and use it for classification.

Generating a TensorFlow model that predicts values in a sinewave

The Bird Species Classifier is an application built using a Convolutional Neural Network (CNN) to classify images of birds into one of 525 different species. It allows users to upload an image of a bird and receive a prediction of the bird species. Along with analysing the performance of various optimising algorithms.

Learning about the algorithms used in machine learning, along with techniques for training and testing models.

A NumPy based Neural Network Package Implementation

En el siguiente repositorio encontrarás material relacionado con optimizadores usados en metodologías de aprendizaje de maquina. In the following repository you'll find examples of optimizers used in machine learning methods

Experimented with and compared DFW neural network optimizer with SGD and ADAM on both vision and language tasks

Introduction to the Adam Optimizer with examples

Unofficial implementation of the Adan optimizer with Schedule-Free

Pytorch library to test optimizers by visualizing how they descend on a your images. You can draw your own custom loss landscape and see what different optimizers do.