Ever wondered how to code your Neural Network using NumPy, with no frameworks involved?

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

Machine learning algorithms in Dart programming language

Implementation of a series of Neural Network architectures in TensorFow 2.0

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

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

Gradient Descent(From Scratch & With TensorFlow)

Advanced Twitter sentiment analysis pipeline using Apache Spark for distributed data processing, featuring TF-IDF–based feature engineering and stochastic gradient-descent classification for scalable, real-time sentiment insights.

All about machine learning

Compilation of different ML algorithms implemented from scratch (and optimized extensively) for the courses COL774: Machine Learning (Spring 2020) & COL772: Natural Language Processing (Fall 2020)

线性回归算法，close-form, batch 梯度下降，mini-batch 梯度下降，随机梯度下降，RMSE

The laboratory from CLOUDS Course at EURECOM

PyTorch Implementation of Optimizers for Deep Learning from scratch.

Batch GD & Stochastic GD built from scratch using only NumPy — not to chase a high R² score, but to understand how a model actually learns. Gradients derived manually from MSE loss, weights updated step by step, convergence visualized live with animated charts.

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

This repository includes implementation of the basic optimization algorithms (Batch-Mini-stochatic)Gradient descents and NAG,Adagrad,RMSProp and Adam)

Naive Bayes classifier and Logistic Regression classifier to predict whether a transaction is fraudulent or not

Numerical Optimization for Machine Learning & Data Science

Implementation of linear regression with L2 regularization (ridge regression) using numpy.

Following and implementing (some of) the machine learning algorithms from scratch based on the Stanford CS229 course.

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.

Softmax Regression from scratch. MNIST dataset

developed a model that can predict air temperature according to atmospheric pressure.

No description provided.

A practical comparison of gradient descent algorithms to predict student performance using study and lifestyle data, with visual analysis.

Analyzing and overcoming the curse of dimensionality and exploring various gradient descent techniques with implementations in R

Gradient Descent is a technique used to fine-tune machine learning algorithms with differentiable loss functions. It's an open-ended mathematical expression, tirelessly calculating the first-order derivative of a loss function and making precise parameter adjustments.

Just exploring Deep Learning

Linear Regression - Batch Gradient Descent

Implement Linear_Regression class and experiment with Batch,Mini Batch and Stohastic Gradient Descent!