Custom Optimizer in TensorFlow(定义你自己的Tensorflow Optimizer)

Reproducing the paper "PADAM: Closing The Generalization Gap of Adaptive Gradient Methods In Training Deep Neural Networks" for the ICLR 2019 Reproducibility Challenge

Quasi Hyperbolic Rectified DEMON Adam/Amsgrad with AdaMod, Gradient Centralization, Lookahead, iterative averaging and decorrelated Weight Decay

[Python] [arXiv/cs] Paper "An Overview of Gradient Descent Optimization Algorithms" by Sebastian Ruder

Nadir: Cutting-edge PyTorch optimizers for simplicity & composability! 🔥🚀💻

The optimization methods in deep learning explained by Vietnamese such as gradient descent, momentum, NAG, AdaGrad, Adadelta, RMSProp, Adam, Adamax, Nadam, AMSGrad.

A Repository to Visualize the training of Linear Model by optimizers such as SGD, Adam, RMSProp, AdamW, ASMGrad etc

Implementation and comparison of zero order vs first order method on the AdaMM (aka AMSGrad) optimizer: analysis of convergence rates and minima shape

Fully connected neural network for digit classification using MNIST data

A comparison between implementations of different gradient-based optimization algorithms (Gradient Descent, Adam, Adamax, Nadam, Amsgrad). The comparison was made on some of the most common functions used for testing optimization algorithms.

Generalization of Adam, AdaMax, AMSGrad algorithms for PyTorch

"Simulations for the paper 'A Review Article On Gradient Descent Optimization Algorithms' by Sebastian Roeder"

Deep Learning Optimizers

The implementation of the algorithm shows that OPTIMISTIC-AMSGRAD improves AMSGRAD in terms of various measures: training loss, testing loss, and classification accuracy on training/testing data over epochs.