Search Results for "regression" - Page 8
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Showing 358 open source projects for "regression"
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TensorFlow Machine Learning Cookbook
Code for Tensorflow Machine Learning Cookbook
...Each section focuses on a different aspect of machine learning development, including tensor manipulation, model training, optimization strategies, and data processing techniques. The examples illustrate how TensorFlow operations and tensors can be used to build machine learning pipelines and perform tasks such as regression, classification, and clustering. By combining theoretical explanations with executable code, the project helps developers understand how TensorFlow algorithms operate internally while also providing working examples that can be adapted for real projects. -
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Machine Learning with TensorFlow
Accompanying source code for Machine Learning with TensorFlow
...The project provides numerous code samples demonstrating how to build machine learning models using the TensorFlow framework. These examples illustrate core machine learning concepts such as regression, classification, clustering, and neural networks through practical implementations. The repository includes implementations of algorithms such as logistic regression, convolutional neural networks, and autoencoders, which allow readers to experiment with different learning techniques. Many examples are structured as standalone scripts or notebooks that can be executed directly to reproduce the results described in the book. ... -
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Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models
...The goal of the project is to help learners understand how machine learning algorithms work internally by building them step by step from fundamental mathematical operations. The repository includes implementations of algorithms ranging from simple models such as linear regression and logistic regression to more complex techniques such as decision trees, support vector machines, clustering methods, and neural networks. Because the code avoids external machine learning libraries, it exposes the full logic behind model training, optimization, and prediction processes. The project also provides examples and explanations that illustrate how the algorithms behave and how different components interact during training. -
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Scikit-learn Tutorial
An introductory tutorial for scikit-learn
...It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. ... -
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Dive-into-DL-TensorFlow2.0
Dive into Deep Learning
This project changes the MXNet code implementation in the original book "Learning Deep Learning by Hand" to TensorFlow2 implementation. After consulting Mr. Li Mu by the tutor of archersama , the implementation of this project has been agreed by Mr. Li Mu. Original authors: Aston Zhang, Li Mu, Zachary C. Lipton, Alexander J. Smola and other community contributors. There are some differences between the Chinese and English versions of this book . This project mainly focuses on TensorFlow2... -
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Deep-Learning-with-PyTorch-Tutorials
Deep Learning and PyTorch Introduction Video Tutorial with Source Code
...The lessons begin with PyTorch setup, tensors, indexing, mathematical operations, gradients, and basic optimization. They then move into neural networks, logistic regression, multilayer perceptrons, CNNs, ResNet, RNNs, LSTMs, autoencoders, VAEs, GANs, graph convolutional networks, and transfer learning. The repository is designed for learners who want to connect deep learning concepts with executable examples. Overall, it is a structured PyTorch practice resource for beginners and early deep learning practitioners. -
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GSMLBook
Recipes for basic machine learning algorithms using sklearn in jupyter
...The emphasis is primarily on learning to use existing libraries such as Scikit-Learn with easy recipes and existing data files that can found on-line. Topics include linear, multilinear, polynomial, stepwise, lasso, ridge, and logistic regression; ROC curves and measures of binary classification; nonlinear regression (including an introduction to gradient descent); classification and regression trees; random forests; neural networks; probabilistic methods (KNN, naive Bayes', QDA, LDA); dimensionality reduction with PCA; support vector machines; and clustering with K-Means, hierarchical, and DBScan. ... -
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Coursera Machine Learning
Coursera Machine Learning By Prof. Andrew Ng
...It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes week-by-week course schedules with links to exercises, lecture notes, and additional resources. Alongside the official coursework, the repository includes supplemental explanations, code snippets, and references to recommended textbooks and external materials. By gathering course-related resources into a single space, this project acts as a practical study companion for learners revisiting or supplementing the original course. -
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AET
Detects visual changes on websites and performs page health checks
AET is a system that detects visual changes on websites and performs basic page health checks (like w3c compliance, accessibility, HTTP status codes, JS Error checks and others). AET is designed as a flexible system that can be adapted and tailored to the regression requirements of a given project. The tool has been developed to aid front-end client-side layout regression testing of websites or portfolios, in essence assessing the impact or change of a website from one snapshot to the next. -
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benchm-ml
A benchmark of commonly used open source implementations
...It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. The repository is structured in logical folders, each corresponding to algorithm categories. -
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RGI (RCP scenario-based Growing degree days Interpolation) program is to predicting and interpolating average temperature and Growth Degree Day (GDD) using regression analysis. This program is free but we require a citation of the following paper when using RGI. reference: (not yet)
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NUT Nutrition Software
nutrition software to record and analyze what you eat
nut is nutrition software to record what you eat and analyze your meals for nutrient composition. The emphasis is on personal dietary experimentation to determine the best possible diet. The software uses SQLite for a portable, os-independent application. -
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Wraith
A responsive screenshot comparison tool
Wraith is a screenshot comparison tool, created by developers at BBC News. Wraith uses a headless browser to create screenshots of webpages on different environments (or at different moments in time) and then creates a diff of the two images; the affected areas are highlighted in blue. There are two main modes for using Wraith, 'capture' mode and 'history' mode. Wraith has some built-in JavaScript and configuration file templates for you to get started with right away. If you wish to take... -
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Nightwatch VRT
Visual Regression Testing tools for nightwatch.js
Nightwatch Visual Regression Testing tools for nightwatch.js. Nightwatch VRT extends nightwatch.js with an assertion that captures a screenshot of a DOM element identified by a selector and compares the screenshot against a baseline screenshot. If the baseline screenshot does not exist, it will be created the first time you run the test and the assertion will pass. -
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Active Learning
Framework and examples for active learning with machine learning model
...It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
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CodelPlant
Codelplant stat. multi-regression M-learning for leaves segmentation
CodelPlant is based on the creation of a statistical multi-regression decision tree from data obtained by pixel RGB-HSB machine learning analysis. The model is training with human observation, so the human factor is being entered the process. -
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spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
...Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s machine learning package. The repository contains detailed analyses of various algorithms including classification, regression, clustering, dimensionality reduction, and recommendation systems. Each section discusses both the mathematical principles behind the algorithms and how Spark implements them in a distributed computing environment. By studying these implementations, readers gain insight into how large-scale machine learning pipelines operate across distributed data systems. -
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BigDecimal
Arithmetic and calculus with decimal numbers of arbitrary precision.
BigDecimal is a C # library that uses System.Numerics.BigInteger in its implementation, adding only a decimal place quantizer. In BigDecimal all the arithmetic operations are implemented, including, logarithms, systems resolution of linear equations, trigonometric functions, polynomial regression, hyperbolic functions, the notorious gamma function (factorial for non-integer real numbers) and more that will be implemented still. My intention is, at a minimum, to implement all the mathematical functions of the Windows calculator, and at most, if any, all the mathematical functions of Office Excel. -
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BackstopJS
Catch CSS curve balls
Visual regression testing for web apps. Supports screen rendering with Chrome-headless. Add your own interactions with the Playwright and Puppeteer scripting. BackstopJS automates visual regression testing of your responsive web UI by comparing DOM screenshots over time. -
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Python Data Science Tutorials
Common data analysis and machine learning tasks using python
...The collection begins with Python fundamentals and then moves into scientific computing, statistics, NumPy, pandas, data exploration, and visualization. Its machine learning sections cover practical algorithms and libraries, including regression, classification, clustering, support vector machines, and computer vision resources. Additional material addresses text mining, sentiment analysis, serialization with pickle, AutoML, regular expressions, and web scraping. The repository is organized by topic so learners can use it as a study roadmap or troubleshooting index. Many links document the earlier Python data science ecosystem, making the project especially valuable as a broad historical resource collection. -
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rene file renamer
Windows/Linux lexical/semantic multiple file renamer
Rene is an extended file renamer command line program written in Python for Windows and Linux. Its goal is to be easy to use but powerful. For basic use, the command syntax is as simple as Windows ren and Linux mv yet affords greater flexibility in reusing parts of the original name and formulating its replacement. Features include: -- Automatic name adjustment to avoid existing file names with a choice of parameterized collision avoidance schemas. -- Semantic name selection, for example,... -
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Oryx
Lambda architecture on Apache Spark, Apache Kafka for real-time
Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large-scale machine learning. It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. The application is written in Java, using Apache Spark, Hadoop, Tomcat, Kafka, Zookeeper and more. Configuration uses a single Typesafe Config config file, wherein applications configure an entire deployment of the system. This includes implementations of key interface classes which implement the batch, speed, and serving logic. ... -
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Acceptance/regression testing framework based on full screen automation (via terminal emulation) with integrated unit testing engine (standard Ruby test/unit). Moved to: https://github.com/tsc-collection/tsc-act Now part of GitHub's TSC Collection available at https://github.com/tsc-collection
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Firing Range
Firing Range is a test bed for web application security scanners
...Each scenario is crafted to reflect how bugs appear in production—behind frameworks, in odd encodings, or across redirects—so scanners must demonstrate accurate crawling and context understanding. Because the behaviors are stable and documented, teams can run comparative tests over time and quantify regression or improvement in their pipelines. It’s equally useful for human training, giving analysts a safe playground to practice exploitation and triage skills. -
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Lihang
Statistical learning methods (2nd edition) [Li Hang]
...The repository aims to help readers understand the theoretical foundations of machine learning algorithms through practical implementations and detailed explanations. It includes notebooks and scripts that demonstrate how key algorithms such as perceptrons, decision trees, logistic regression, support vector machines, and hidden Markov models work in practice. In addition to code examples, the project contains supplementary materials such as formula references, glossaries of technical terms, and documentation explaining mathematical notation used throughout the algorithms. The repository also provides links to related research papers and references that expand on the theoretical background presented in the book.
