Open Source Machine Learning Software - Page 18
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Watermark-Removal
Machine learning image inpainting task that removes watermarks
Watermark-Removal repository is a machine learning project focused on removing visible watermarks from digital images using deep learning and image inpainting techniques. The system analyzes an image containing a watermark and attempts to reconstruct the underlying visual content so that the watermark is removed while preserving the original appearance of the image. The project uses neural network models inspired by research in contextual attention and gated convolution, which are methods commonly applied to image restoration tasks. Through these techniques, the model learns to identify regions of the image affected by the watermark and generate realistic replacements for the missing visual information. The repository contains code for preprocessing images, training the model, and running inference on images to automatically remove watermark artifacts. -
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Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
Zero to Mastery Machine Learning is an open-source repository that contains the complete course materials for the Zero to Mastery Machine Learning and Data Science bootcamp. The project provides a structured curriculum designed to teach machine learning and data science using Python through hands-on projects and interactive notebooks. The repository includes datasets, Jupyter notebooks, documentation, and example code that walk learners through the entire machine learning workflow from problem definition to model deployment. The course introduces essential tools such as NumPy, pandas, Matplotlib, and scikit-learn before moving on to deep learning with frameworks like TensorFlow and Keras. It also includes milestone projects that demonstrate how to build end-to-end machine learning systems using real datasets, including classification and regression tasks. -
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auto_ml
Automated machine learning for analytics & production
auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. Before you go any further, try running the code. Load up some data (either a DataFrame, or a list of dictionaries, where each dictionary is a row of data). Make a column_descriptions dictionary that tells us which attribute name in each row represents the value we’re trying to predict. Pass all that into auto_ml, and see what happens! You can pass in your own function to perform feature engineering on the data. This will be called as the first step in the pipeline that auto_ml builds out. You will be passed the entire X dataset (not the y dataset), and are expected to return the entire X dataset. The advantage of including it in the pipeline is that it will then be applied to any data you want predictions on later. -
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elasticsearch-learning-to-rank
Plugin to integrate Learning to Rank
The Elasticsearch Learning to Rank plugin uses machine learning to improve search relevance ranking. It's powering search at places like Wikimedia Foundation and Snagajob. -
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fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. -
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hloc
Visual localization made easy with hloc
This is hloc, a modular toolbox for state-of-the-art 6-DoF visual localization. It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using SfM. Just download the datasets and you're reading to go! The notebook pipeline_InLoc.ipynb shows the steps for localizing with InLoc. It's much simpler since a 3D SfM model is not needed. We show in pipeline_SfM.ipynb how to run 3D reconstruction for an unordered set of images. This generates reference poses, and a nice sparse 3D model suitable for localization with the same pipeline as Aachen. -
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mlpack
mlpack: a scalable C++ machine learning library
mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. In addition to its powerful C++ interface, mlpack also provides command-line programs, Python bindings, Julia bindings, Go bindings and R bindings. Written in C++ and built on the Armadillo linear algebra library, the ensmallen numerical optimization library, and parts of Boost. Aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. mlpack uses CMake as a build system and allows several flexible build configuration options. You can consult any of the CMake tutorials for further documentation, but this tutorial should be enough to get mlpack built and installed. -
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python-small-examples
Focus on creating classic Python small examples and cases
python-small-examples is an open-source educational repository that contains hundreds of concise Python programming examples designed to illustrate practical coding techniques. The project focuses on teaching programming concepts through small, focused scripts that demonstrate common tasks in data processing, visualization, and general programming. Each example highlights a specific function or programming pattern so that learners can quickly understand how to apply Python features in real-world scenarios. The repository includes examples covering topics such as file processing, JSON manipulation, data visualization, and library usage. The examples are intentionally short and easy to read, making them useful for beginners who want to understand Python syntax and programming logic step by step. The repository is organized as a large collection of small scripts and notes that can be browsed individually without needing to study a full project. -
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snntorch
Deep and online learning with spiking neural networks in Python
snntorch is a deep learning library that enables researchers and developers to build and train spiking neural networks using the PyTorch framework. Spiking neural networks are biologically inspired models that communicate through discrete spike events rather than continuous activation values, making them closer to how neurons operate in the brain. The library extends PyTorch’s tensor computation capabilities to support gradient-based learning for networks composed of spiking neurons. This allows researchers to train spiking neural models using familiar deep learning workflows while taking advantage of GPU acceleration and automatic differentiation. snnTorch provides implementations of common spiking neuron models, surrogate gradient training methods, and utilities for handling temporal neural dynamics. Because spiking neural networks operate over time and encode information through spike timing, the library includes tools for simulating temporal behavior. -
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stanford-tensorflow-tutorials
This repository contains code examples for the Stanford's course
This repository contains code examples for the course CS 20: TensorFlow for Deep Learning Research. It will be updated as the class progresses. Detailed syllabus and lecture notes can be found in the site. For this course, I use python3.6 and TensorFlow 1.4.1. -
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OpenNN - Open Neural Networks Library
Machine learning algorithms for advanced analytics
OpenNN is a software library written in C++ for advanced analytics. It implements neural networks, the most successful machine learning method. Some typical applications of OpenNN are business intelligence (customer segmentation, churn prevention…), health care (early diagnosis, microarray analysis…) and engineering (performance optimization, predictive maitenance…). OpenNN does not deal with computer vision or natural language processing. The main advantage of OpenNN is its high performance. This library outstands in terms of execution speed and memory allocation. It is constantly optimized and parallelized in order to maximize its efficiency. The documentation is composed by tutorials and examples to offer a complete overview about the library. OpenNN is developed by Artelnics, a company specialized in artificial intelligence. -
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AminePlatform
Amine is a Multi-Layer Platform for the dev. of Intelligent Systems
Amine is an Artificial Intelligence Multi-Layer Java Open Source Platform dedicated to the development of various kinds of Intelligent Systems and Agents (Knowledge-Based, Ontology-Based, Conceptual Graph -CG- Based, NLP, Reasoning and Learning, Natural Language Processing, etc.). Ontology, KB can be created and manipulated with various processes. CG theory is used as the main knowledge representation language. Amine provides two languages: PROLOG+CG which extends PROLOG with CG and Amine modules, and SYNERGY which is a visual activation/propagation based language. CGs are considered by SYNERGY as activable/executable graphs. See for more detail: //amine-platform.sourceforge.net/ -
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Clustering Variation looks for a good subset of attributes in order to improve the classification accuracy of supervised learning techniques in classification problems with a huge number of attributes involved. It first creates a ranking of attributes based on the Variation value, then divide into two groups, last using Verification method to select the best group.
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Eventer
Rapid, unbiased, reproducible analysis of synaptic events
Eventer is a programme designed for the detection of spontaneous synaptic events measured by electrophysiology or imaging. The software combines deconvolution for detection, and variable length template matching approaches for screening out false positive events. Eventer also includes a machine learning-based approach allowing users to train a model to implement their ‘expert’ selection criteria across data sets without bias. Sharing models allows users to implement consistent analysis procedures. The software is coded in MATLAB, but has been compiled as standalone applications for Windows, Mac and Linux. Please visit the official Eventer website for more info https://eventerneuro.netlify.app/ While the paper is in preparation, please cite as; Winchester, G., Liu, S., Steele, O.G., Aziz, W. and Penn, A.C. (2020) Eventer. Software for the detection of spontaneous synaptic events measured by electrophysiology or imaging. http://doi.org/10.5281/zenodo.3991676 -
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AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
AI-Engineer-Headquarters is a comprehensive educational repository designed to help developers become advanced AI engineers through a structured learning path and practical system-building exercises. The project serves as a curated collection of resources, methodologies, and tools covering topics across the entire artificial intelligence development lifecycle. Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real systems that incorporate machine learning, large language models, data pipelines, and AI infrastructure. The curriculum includes a progression of topics such as foundational AI engineering skills, machine learning systems design, large language model usage, retrieval-augmented generation systems, model fine-tuning, and autonomous AI agents. It also promotes disciplined learning routines and project-based practice so learners can develop practical experience and build deployable solutions. -
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AIF360
A comprehensive set of fairness metrics for datasets
This extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. We invite you to use and improve it. The AI Fairness 360 toolkit is an extensible open-source library containing techniques developed by the research community to help detect and mitigate bias in machine learning models throughout the AI application lifecycle. AI Fairness 360 package is available in both Python and R. The AI Fairness 360 interactive experience provides a gentle introduction to the concepts and capabilities. The tutorials and other notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available. Being a comprehensive set of capabilities, it may be confusing to figure out which metrics and algorithms are most appropriate for a given use case. To help, we have created some guidance material that can be consulted. -
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AIGC-Interview-Book
AIGC algorithm engineer interview secrets
AIGC-Interview-Book is a large educational repository designed to help engineers prepare for technical interviews related to artificial intelligence and generative AI roles. The project compiles knowledge from industry practitioners and researchers into a structured reference covering the AI ecosystem. Topics included in the repository span large language models, generative AI systems, traditional deep learning methods, reinforcement learning, computer vision, natural language processing, and machine learning theory. In addition to technical concepts, the repository also contains interview preparation materials such as practice questions, hiring insights, and career advice for AI engineers. The materials are organized so readers can study fundamental topics as well as advanced research areas that frequently appear in technical interviews. -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. Albumentations supports different computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation. Albumentations works well with data from different domains: photos, medical images, satellite imagery, manufacturing and industrial applications, Generative Adversarial Networks. Albumentations can work with various deep learning frameworks such as PyTorch and Keras. -
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AlphaTree
DNN && GAN && NLP && BIG DATA
AlphaTree is an educational repository that provides a visual roadmap of deep learning models and related artificial intelligence technologies. The project focuses on explaining the historical development and relationships between major neural network architectures used in modern machine learning. It presents diagrams and documentation describing the evolution of models such as LeNet, AlexNet, VGG, ResNet, DenseNet, and Inception networks. The repository organizes these architectures into a structured learning path that helps learners understand how deep learning models improved over time through changes in depth, architectural complexity, and training techniques. In addition to neural networks used for image classification, the project also references broader AI fields such as generative adversarial networks, natural language processing, and graph neural networks. -
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AutoTrain Advanced
Faster and easier training and deployments
AutoTrain Advanced is an open-source machine learning training framework developed by Hugging Face that simplifies the process of training and fine-tuning state-of-the-art AI models. The project provides a no-code and low-code interface that allows users to train models using custom datasets without needing extensive expertise in machine learning engineering. It supports a wide range of tasks including text classification, sequence-to-sequence modeling, token classification, sentence embedding training, and large language model fine-tuning. The system integrates closely with the Hugging Face ecosystem and allows developers to train models using datasets hosted on the Hugging Face Hub. AutoTrain Advanced can run locally or in cloud environments, making it adaptable to different computational setups. By automating tasks such as model configuration, hyperparameter selection, and training pipelines, the project significantly reduces the technical barrier to building AI systems. -
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Awesome AI-ML-DL
Awesome Artificial Intelligence, Machine Learning and Deep Learning
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics. This repo is dedicated to engineers, developers, data scientists and all other professions that take interest in AI, ML, DL and related sciences. To make learning interesting and to create a place to easily find all the necessary material. Please contribute, watch, star, fork and share the repo with others in your community. -
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Awesome Community Detection Research
A curated list of community detection research papers
A collection of community detection papers. A curated list of community detection research papers with implementations. Similar collections about graph classification, classification/regression tree, fraud detection, and gradient boosting papers with implementations. -
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Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
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Awesome production machine learning
Curated list of awesome open source libraries
This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning. Open-source frameworks, tutorials, and articles curated by machine learning professionals. Open-source bias audit toolkits for data scientists, machine learning researchers, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive risk-assessment tools. -
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Bayesian machine learning notebooks
Notebooks about Bayesian methods for machine learning
Notebooks about Bayesian methods for machine learning.
