Gorgonia is a library that helps facilitate machine learning in Go.

Fast and Easy Infinite Neural Networks in Python

interactive visualization of 5 popular gradient descent methods with step-by-step illustration and hyperparameter tuning UI

[3DV 2025] Code for "FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent" by Cameron Smith*, David Charatan*, Ayush Tewari, and Vincent Sitzmann

OptimLib: a lightweight C++ library of numerical optimization methods for nonlinear functions

Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.

A graph based, trait pluggable MEV bundle generator with quadratic gradient descent price optimization implemented in Rust.

A TensorFlow-inspired neural network library built from scratch in C# 7.3 for .NET Standard 2.0, with GPU support through cuDNN

Example demonstrating how gradient descent may be used to solve a linear regression problem

🧩 Shape-Safe Symbolic Differentiation with Algebraic Data Types

Image morphing without reference points by applying warp maps and optimizing over them.

With Uno, finally take full control of your SQP/barrier solver for nonlinearly constrained optimization

code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"

Implementations of the algorithms described in Differentiable plasticity: training plastic networks with gradient descent, a research paper from Uber AI Labs.

The simplest form of an artificial neural network explained and demonstrated.

This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.

Code for our NeurIPS 2022 paper

NMFLibrary: Non-negative Matrix Factorization (NMF) Library: Version 2.1

Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".

Implementing Multiple Layer Neural Network from Scratch

A PyTorch implementation of Learning to learn by gradient descent by gradient descent

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

This is a list of peer-reviewed representative papers on deep learning dynamics (optimization dynamics of neural networks). The success of deep learning attributes to both network architecture and stochastic optimization. Thus, deep learning dynamics play an essentially important role in theoretical foundation of deep learning.

深度学习常用优化方法详解

Six snippets of code that made deep learning what it is today.

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

👩🏻‍⚕️Covid-19 estimation and forecast using statistical model; 新型冠状病毒肺炎统计模型预测 (Jan 2020)

Implementation of Estimating Training Data Influence by Tracing Gradient Descent (NeurIPS 2020)

Implementation of Factorization Machines on Spark using parallel stochastic gradient descent (python and scala)

A curated list of mathematics documents ,Concepts, Study Materials , Algorithms and Codes available across the internet for machine learning and deep learning