There are 3 repositories under learning-rate-scheduling topic.
Learning Rate Warmup in PyTorch
optimizer & lr scheduler & loss function collections in PyTorch
Polynomial Learning Rate Decay Scheduler for PyTorch
A guide that integrates Pytorch DistributedDataParallel, Apex, warmup, learning rate scheduler, also mentions the set-up of early-stopping and random seed.
Pytorch cyclic cosine decay learning rate scheduler
Warmup learning rate wrapper for Pytorch Scheduler
sharpDARTS: Faster and More Accurate Differentiable Architecture Search
Keras Callback to Automatically Adjust the learning rate when it stops improving
Pytorch implementation of arbitrary learning rate and momentum schedules, including the One Cycle Policy
A lightweight but efficient Transformer model for accurate univariate stock price forecasting, designed for real-time trading applications. This project transforms the vanilla Transformer architecture for higher-precision financial time series analysis with minimal computational demands.
Implementation of fluctuation dissipation relations for automatic learning rate annealing.
(GECCO2023 Best Paper Nomination) CMA-ES with Learning Rate Adaptation
Comprehensive image classification for training multilayer perceptron (MLP), LeNet, LeNet5, conv2, conv4, conv6, VGG11, VGG13, VGG16, VGG19 with batch normalization, ResNet18, ResNet34, ResNet50, MobilNetV2 on MNIST, CIFAR10, CIFAR100, and ImageNet1K.
End-to-end Image Classification using Deep Learning toolkit for custom image datasets. Features include Pre-Processing, Training with Multiple CNN Architectures and Statistical Inference Tools. Special utilities for RAM optimization, Learning Rate Scheduling, and Detailed Code Comments are included.
A method for assigning separate learning rate schedulers to different parameters group in a model.
In this repository, I put into test my newly acquired Deep Learning skills in order to solve the Kaggle's famous Image Classification Problem, called "Dogs vs. Cats".
Build from scratch
Master's thesis: Experiments on multistage step size schedulers for first-order optimization in minimax problems
Visualize the progress of the learning rate scheduler graphically.
Code in MATLAB for 1st order optimization algorithms implemented for elastic net regularized convex objective functions.
Flexible parameter scheduler that can be implemented with proprietary and open source optimizers.
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.
Submission Akhir - Image Classification Model Deployment - Belajar Pengembangan Machine Learning - Dicoding
The machine learning task in this assignment is image classification using Convolutional Neural Networks in Tensorflow and Keras
SPECTRA: Solar Panel Evaluation through Computer Vision and Advanced Techniques for Reliable Analysis
Semester project on the impact of label noise on deep learning optimization