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
[PENDING] The official repo for the paper: "A Lightweight Multi-Head Attention Transformer for Stock Price Forecasting".
Pytorch implementation of arbitrary learning rate and momentum schedules, including the One Cycle Policy
Implementation of fluctuation dissipation relations for automatic learning rate annealing.
A method for assigning separate learning rate schedulers to different parameters group in a model.
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.
(GECCO2023 Best Paper Nomination) CMA-ES with Learning Rate Adaptation
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.
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.
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
This project conducts a thorough analysis of weather time series data using diverse statistical and deep learning models. Each model was rigorously applied to the same weather time series data to assess and compare their forecasting accuracy. Detailed results and analyses are provided to delineate the strengths and weaknesses of each approach.
Semester project on the impact of label noise on deep learning optimization
Used different Transformer based and LSTM based models for forecasting rainfall in different areas of Mumbai. Employed different smart training techniques to improve correlation with the true time-series.