optimizer-visualization

A learning rate range test implementation in PyTorch

Play deep learning with CIFAR datasets

Improving MMD-GAN training with repulsive loss function

PyTorch implementation of some learning rate schedulers for deep learning researcher.

A method for assigning separate learning rate schedulers to different parameters group in a model.

This repository focuses on the Neural Networks and deep learning. It is a workbook you can refer it for a reference. I'll Include following content here: Neurons, perceptrons, weight and biases, learning rate, activation form, hyper parameters, RNN, CNN and other popular concepts.

Analyze the performance of 7 optimizers by varying their learning rates

Neural net solver with auto-tuned hyperparameters (Python 3.5)

This project involves training a machine learning model and plotting its learning curves to analyze training and testing accuracies, utilizing Java for model execution and Python for data visualization. It includes commands for compiling and running the Java program, generating plots, and sending results via email.