Implementation of basic ML algorithms from scratch in python...

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

MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

Machine learning algorithms in Dart programming language

Pytorch implementation of preconditioned stochastic gradient descent (Kron and affine preconditioner, low-rank approximation preconditioner and more)

Classifying the Blur and Clear Images

Riemannian stochastic optimization algorithms: Version 1.0.3

Matlab implementation of the Adam stochastic gradient descent optimisation algorithm

Visualization of various deep learning optimization algorithms using PyTorch automatic differentiation and optimizers.

Easy-to-use linear and non-linear solver

Exploiting Explainable Metrics for Augmented SGD [CVPR2022]

OnLine Low-rank Subspace tracking by TEnsor CP Decomposition in Matlab: Version 1.0.1

Python implementation of stochastic sub-gradient descent algorithm for SVM from scratch

XCSF learning classifier system: rule-based online evolutionary machine learning

AAAI & CVPR 2016: Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD)

A statistical computations and ML orientated Python package to predict stock price.

Tensorflow implementation of preconditioned stochastic gradient descent

Hi! Thanks for checking out my tutorial where I walk you through the process of coding a convolutional neural network in java from scratch. After building a network for a university assignment, I decided to create a tutorial to (hopefully) help others do the same and improve my own understanding of neural networks.

SVM with Learning Using Privileged Information (LUPI) framework

Stochastic gradient descent with model building

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

A basic neural network with backpropagation programmed from scratch in C++

Implement a Neural Network trained with back propagation in Python

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

Slides and notebooks for my tutorial at PyData London 2018

Recommend Restaurants to User based on the ratings given by them to the restaurants

Wrote a neural network that uses fundamental DL algorithms to identify handwritten digits from MNIST dataset.

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Simple MATLAB toolbox for deep learning network: Version 1.0.3

Stochastic Gradient Descent (SGD) is an optimization algorithm that updates model parameters iteratively using small, random subsets (batches) of data, rather than the entire dataset. It significantly speeds up training for large datasets, though it introduces noise that causes, in some cases, heavy fluctuations.deep learning/neural networks.solver