mini-batch-gradient-descent

aditya9211/SVHN-CNN

Google Street View House Number(SVHN) Dataset, and classifying them through CNN

bhattbhavesh91/gradient-descent-variants

My implementation of Batch, Stochastic & Mini-Batch Gradient Descent Algorithm using Python

SSQ/Coursera-Ng-Improving-Deep-Neural-Networks-Hyperparameter-tuning-Regularization-and-Optimization

Short description for quick search

SSQ/Coursera-UW-Machine-Learning-Classification

Notebook for quick search

Coursera-Students-Community/Deep-Learning-Specialization

Coursera - Deep Learning Specialization - deeplearning.ai

ChanchalKumarMaji/Coursera-Deep-Learning-Specialization-deeplearning.ai

[Coursera] Deep Learning Specialization on Coursera

DunittMonagas/Improving-Deep-Neural-Networks-Hyperparameter-tuning-Regularization-and-Optimization

Curso Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Segundo curso del programa especializado Deep Learning. Este repositorio contiene todos los ejercicios resueltos. https://www.coursera.org/learn/neural-networks-deep-learning

aditya9211/MNIST-Classification-with-NeuralNet

MNIST Handwritten Digits Classification using 3 Layer Neural Net 98.7% Accuracy

kaustubholpadkar/Predicting-House-Price-using-Multivariate-Linear-Regression

Predicting House Price from Size and Number of Bedrooms using Multivariate Linear Regression in Python from scratch

mahendranandi/Optimization-Algorithm

For learning, visualizing and understanding the optimization techniques and algorithms.

MoinDalvs/Gradient_Descent_For_beginners

Gradient_descent_Complete_In_Depth_for beginners

NvsYashwanth/machinelearningmaster

All about machine learning

SawyerAlston/MNIST-NN-Pure-Math

A "from-scratch" 2-layer neural network for MNIST classification built in pure NumPy, featuring mini-batch gradient descent, momentum, L2 regularization, and evaluation tools — no ML libraries used.

flowstateeng/Coursera-Deep-Learning

A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.

longtng/Stochastic-Gradient-Descent

The laboratory from CLOUDS Course at EURECOM

lucassa3/PEGASOS-SVM-CLASSIFIER

Implementation of a support vector machine classifier using primal estimated sub-gradient solver in C++ and CUDA for NVIDIA GPUs

mstrand1/Jax-logistic-regression

Logistic regression using JAX to support GPU acceleration

annieyan/Stochastic_dual_coordinate_ascent

classify mnist datasets using ridge regression, optimize the algorithem with SGD, stochastic dual coordinate ascent, and mini-batching

AsmaaEssamSultan/Optimization-Techniques-from-scratch

chen-bowen/Deep_Neural_Networks

This project explored the Tensorflow technology, tested the effects of regularizations and mini-batch training on the performance of deep neural networks

DeepraMazumder/Social-Network-Ads-Prediction-Analysis

A Machine Learning project to predict user interactions with social network ads using demographic data to optimize ad targeting

ehtisham-sadiq/Numerical-optimization-algorithms-in-Machine-Learning

Exploring and Implementing Numerical Optimization Algorithms in Machine Learning, with Python code and mathematical insights.

hager51/Numerical-Optimization

Numerical Optimization for Machine Learning & Data Science

itsmesatwik/deep-learning

Various methods for Deep Learning, SGD and Neural Networks.

jnclink/Custom-NN

Custom implementation of a neural network from scratch using Python

lam1aa/Intent-classification-with-FNN

Implementation of a Feedforward Neural Network for intent classification using only Python and NumPy. The model classifies user intents from text input using the Sonos NLU Benchmark dataset.

mmaric27/BasicDNN

Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.

niaj-a/Machine-Learning

Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.

nisheethjaiswal/ROLLING-DOWN-A-CROWDED-VALLEY-OF-OPTIMIZERS-DEVELOPMENTS-FROM-SGD

Deep Learning Optimizers

Sayed-Hossein-Hosseini/Mountaineers_in_Search_of_the_Global_Minimum

Two mountaineers search for the global minimum of a cost function using different approaches. One represents Stochastic Gradient Descent, taking small, random steps, while the other follows Batch Gradient Descent, making precise moves after full evaluation. This analogy illustrates key optimization strategies in machine learning.

shulavkarki/ANN-Scratch-In-MnsitDigit

ANN Classifier built from scratch used to classify MNIST Digit.

SRI-PRIYAN/MachineLearning

Implementing ML Algorithms using Python and comparing with Standard Library functions

tejaswidabas123/albalone-age-prediction

🐚 Abalone Age Prediction: Dive into Data, Surf on Insights! 📊 Unleash the power of predictive analytics on abalone age estimation! From meticulous data exploration to a showdown of optimization methods, this repo is your gateway to accurate age predictions using physical measurements using Pysaprk. 🌊🔮

WHMHammer/robust-mini-batch-gradient-descent

Robust Mini-batch Gradient Descent models

Raafat-Nagy/Implementations_of_ML_and_DL_Optimizers

This repository provides implementations of numerical optimization algorithms for machine learning and deep learning. It includes clear explanations, mathematical formulas, Python code, and visualizations to help understand the behavior of each optimizer.