90 results for “topic:learning-rate”
A learning rate range test implementation in PyTorch
Play deep learning with CIFAR datasets
Visualize Tensorflow's optimizers.
An easy neural network for Java!
Videos of deep learning optimizers moving on 3D problem-landscapes
PyTorch implementation of some learning rate schedulers for deep learning researcher.
Improving MMD-GAN training with repulsive loss function
FIR & LMS filter implementation in C++ with Python & JAVA wrappers
Cyclic learning rate TensorFlow implementation.
Automatic and Simultaneous Adjustment of Learning Rate and Momentum for Stochastic Gradient Descent
One cycle policy learning rate scheduler in PyTorch
Learning rate multiplier
Benchmarking various Computer Vision models on TinyImageNet Dataset
Stochastic Weight Averaging - TensorFlow implementation
SaLSa Optimizer implementation (No learning rates needed)
Meta Transfer Learning for Few Shot Semantic Segmentation using U-Net
How optimizer and learning rate choice affects training performance
OneCycle LearningRateScheduler & Learning Rate Finder for TensorFlow 2.
Improved Hypergradient optimizers for ML, providing better generalization and faster convergence.
sharpDARTS: Faster and More Accurate Differentiable Architecture Search
Pytorch implementation of arbitrary learning rate and momentum schedules, including the One Cycle Policy
callbacks in ai
Implementation of learning rate finder in TensorFlow
A Warmup Scheduler for Pytorch to achieve the warmup learning rate at the beginning of training.
TensorFlow/Keras implementation of the paper: 'Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates'
A packages containing all popular Learning Rate Schedulers. Implemented in Keras Tensorflow
Q-Learing algorithm solves simple mazes.
This program implements linear regression from scratch using the gradient descent algorithm in Python. It predicts car prices based on selected features and uses a dataset of cars with their respective prices.
Source code for NeurIPS-2024 paper "Where Do Large Learning Rates Lead Us"
Residual Network Experiments with CIFAR Datasets.