GitHub topics: momentum-optimization-algorithm

Repositories

nabeelshan78/deep-nn-from-scratch

A fully vectorized Deep Neural Network (DNN) implementation built from scratch using only NumPy - no deep learning frameworks involved. Covers forward/backward propagation, activation functions, modular architecture, and training with different optimizers - a hands-on deep dive into the fundamentals of deep learning.

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kinit-sk/overshoot

Overshoot: Taking advantage of future gradients in momentum-based stochastic optimization

Language: Python - Size: 488 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 2 - Forks: 0

mkgeiger/neural-network

Simple neural-network supporting most importent features like Convolutional-/Fully Connected network completely written in Ansi C

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SaiCodePro/DFSM-Det

Intelligent Detection for RIS-Assisted MIMO Systems: A First-and-Second Momentum Approach

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aahouzi/Simple_Document_Classification_From_Scratch

Simple Document Classification using Multi Class Logistic Regression & SVM Soft Margin from scratch

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AsmaaEssamSultan/Optimization-Techniques-from-scratch

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aysekonus/ml_optimization_algorithms

Comparsion of Machine Learning optimization algorithms with MNIST dataset

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Efesasa0/mlp-backprop-two-moons

EE456 2022 mini project implementation of two-moons problem using multi-layer-perceptron with back-propagation with analyzing performance of initializing methods and momentum rule

Language: MATLAB - Size: 5.31 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

ravikumarchopra/DL-Assignment-1

This repository contains a python implementation of Feed Forward Neural Network with Backpropagation, along with the example scripts for training the network to classify images from mnist and fashion_mnist datasets from keras.

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skolai/nag-gs

NAG-GS: Nesterov Accelerated Gradients with Gauss-Siedel splitting

Language: Python - Size: 459 KB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 8 - Forks: 1

aliejabbari/Optimizations-ADAM-Momentum-SGD

Python code for Gradient Descent, Momentum, and Adam optimization methods. Train neural networks efficiently.

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AlbertoMarinelli/QRI-ELM-and-ELM-with-Standard-Momentum

In this project it is used a Machine Learning model based on a method called Extreme Learning, with the employment of L2-regularization. In particular, a comparison was carried out between: (A1) which is a variant of incremental extreme learning machine that is QRIELM and (A2) which is a standard momentum descent approach, applied to the ELM.

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Heba-Atef99/ML_optimization_algorithms

This is an implementation of different optimization algorithms such as: - Gradient Descent (stochastic - mini-batch - batch) - Momentum - NAG - Adagrad - RMS-prop - BFGS - Adam Also, most of them are implemented in vectorized form for multi-variate problems

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