Open Source Machine Learning Software - Page 50
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Qix
Machine Learning, Deep Learning, PostgreSQL, Distributed System
Qix is an open-source repository that aggregates resources related to artificial intelligence, machine learning, and deep learning development. The project is designed as a curated index of useful tools, frameworks, research papers, and learning materials that developers can use to explore the AI ecosystem. It organizes information about libraries, tutorials, datasets, and software tools used in modern AI research and development. The repository helps developers quickly discover relevant technologies and educational resources without searching through multiple sources individually. It also includes references to frameworks, model implementations, and related research projects across various machine learning subfields. The project acts as a directory of resources that can guide learners toward useful tools and documentation in the AI domain. -
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Qt Handwriting Recognizing it's a simple Qt GUI interface of a artificial neural network to provide handwrite recognition. This project use FANN (Fast Artificial Neural Network) on first approach.
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QuantResearch
Quantitative analysis, strategies and backtests
QuantResearch is a large educational repository dedicated to quantitative finance, algorithmic trading, and financial machine learning research. The project contains numerous notebooks and research materials demonstrating quantitative analysis techniques used in financial markets. These include implementations of factor models, statistical arbitrage strategies, portfolio optimization methods, and reinforcement learning approaches to trading. The repository also explores financial modeling topics such as vector autoregression, Gaussian mixture models, and option pricing techniques. Many notebooks demonstrate backtesting pipelines that allow users to evaluate trading strategies using historical market data. The project integrates machine learning methods with traditional quantitative finance models, illustrating how statistical techniques can be applied to asset management and trading. -
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R-Portfolio - breakeven optimally diversified investment portfolio. Оптимально диверсифицированный инвестиционный портфель.
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An experimental unsupervised learning method for improvising jazz melodies, based on restricted Boltzmann machines (RBMs) layered into Deep Belief Networks (forms of neural networks).
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RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
RL with PyTorch is a research-oriented repository that provides implementations of deep reinforcement learning algorithms using the PyTorch framework. The project focuses on helping developers and researchers understand reinforcement learning methods by providing clean and reproducible implementations of well-known algorithms. It includes code for popular deep reinforcement learning techniques such as Deep Q-Networks, policy gradient methods, actor-critic architectures, and other modern RL approaches. The repository is structured so that users can easily experiment with different algorithms and training environments. Many examples demonstrate how agents learn to interact with simulated environments through trial and error using reinforcement learning principles. The codebase emphasizes clarity and modular design so that researchers can extend the implementations or use them for experimentation and benchmarking. -
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This RapidMiner-plugin consists of operators for feature selection and classification - mainly on high-dimensional (microarray-) data - and some helper-classes/operators.
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The Information Extraction Plugin allows the use of information extraction techniques within RapidMiner. It can be seen as an interface between natural language and IE- or datamining-methods, by extracting interesting information out of documents.
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Reco-papers
Classic papers and resources on recommendation
Reco-papers is a curated repository that collects influential research papers, technical resources, and industry materials related to recommender systems and recommendation algorithms. The project organizes a large body of literature into thematic sections such as classic recommender systems, exploration-exploitation strategies, deep learning–based recommendation models, and cold-start mitigation techniques. It serves as a reference library for researchers and engineers who want to explore foundational and cutting-edge work in recommendation technologies. The repository includes papers from academic institutions and industry organizations and groups them according to topics such as retrieval and reranking, reinforcement learning in recommendation, and feature engineering infrastructure. By structuring these materials into categories, the project helps practitioners quickly discover relevant research for designing recommendation engines in production environments. -
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Reconcile is an open source research platform for coreference resolution. It combines a large number of open source NLP components and provides extension points for researchers to plug in additional features and techniques.
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Responsible AI Toolbox
Responsible AI Toolbox is a suite of tools providing model
Responsible AI Toolbox is a software framework designed to help developers evaluate and improve the reliability, fairness, and transparency of machine learning systems. The project provides tools that assist in analyzing model behavior, detecting bias, improving robustness, and explaining predictions produced by AI systems. It is designed to integrate with common machine learning frameworks, especially PyTorch, allowing developers to apply responsible AI techniques within existing workflows. The toolbox includes methods for adversarial testing, interpretability analysis, and model diagnostics that help developers understand how models behave under different conditions. These capabilities are particularly important for high-impact domains where AI systems must meet strict reliability and fairness requirements. By offering reusable components and standardized workflows, the framework helps organizations implement responsible AI practices throughout the machine learning lifecycle. -
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RoboBeans is an interface to the "Robocup 2D Soccer Simulation Server" that allows developers to write Robocup teams\agents concentrating on behaviour and AI without having to worry about syntax of communication or network issues.
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SIG Rust
Rust language bindings for TensorFlow
SIG Rust provides idiomatic Rust bindings for TensorFlow, making it possible for developers to work with TensorFlow functionality from within the Rust programming language. Rather than replacing TensorFlow itself, it acts as an integration layer that connects Rust applications to the TensorFlow C API. The repository is designed for developers who want Rust’s performance, safety, and systems programming strengths while still accessing TensorFlow’s machine learning capabilities. It includes setup instructions that explain how the crate can automatically download or compile the required TensorFlow shared libraries, which lowers the barrier to getting started. The project also supports environments where TensorFlow is already installed, giving developers more flexibility in how they configure their systems. Documentation, community discussion resources, and versioned releases indicate that the repository is maintained as a serious language binding. -
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SINGA
A distributed deep learning platform
Apache SINGA is an Apache Top Level Project, focusing on distributed training of deep learning and machine learning models. Various example deep learning models are provided in SINGA repo on Github and on Google Colab. SINGA supports data parallel training across multiple GPUs (on a single node or across different nodes). SINGA supports various popular optimizers including stochastic gradient descent with momentum, Adam, RMSProp, and AdaGrad, etc. SINGA records the computation graph and applies the backward propagation automatically after forward propagation. The optimization of memory are implemented in the Device class. SINGA supports loading ONNX format models and saving models defined using SINGA APIs into ONNX format, which enables AI developers to use models across different libraries and tools. SINGA supports the time profiling of each of the operators buffered in the graph. Half precision is supported to bring benefits. -
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SLING
A natural language frame semantics parser
The aim of the SLING project is to learn to read and understand Wikipedia articles in many languages for the purpose of knowledge base completion, e.g. adding facts mentioned in Wikipedia (and other sources) to the Wikidata knowledge base. We use frame semantics as a common representation for both knowledge representation and document annotation. The SLING parser can be trained to produce frame semantic representations of text directly without any explicit intervening linguistic representation. The SLING project is still work in progress. We do not yet have a full system that can extract facts from arbitrary text, but we have built a number of the subsystems needed for such a system. The SLING frame store is our basic framework for building and manipulating frame semantic graph structures. The Wiki flow pipeline can take a raw dump of Wikidata and convert this into one big frame graph. -
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SPAWNN
SPatial Analysis With self-organizing Neural Networks
The SPAWNN toolkit is an innovative toolkit for spatial analysis with self-organizing neural networks which is particularily useful for spatial analysis, visualization and geographical data mining. To run the toolkit, simply download and execute (double-click) the jar-file. Please cite: - Hagenauer, J., & Helbich, M. (2016). SPAWNN: A Toolkit for SPatial Analysis With Self-Organizing Neural Networks. Transactions in GIS, 20(5), 755-775. Other related publications: - Hagenauer, J. (2016). Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks. Journal of Geographical Systems, 18(1), 1-15. - Hagenauer, J., & Helbich, M. (2013). Contextual neural gas for spatial clustering and analysis. International Journal of Geographical Information Science, 27(2), 251-266. -
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SRU
Training RNNs as Fast as CNNs
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate the training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model on the translation by incorporating SRU into the architecture. The experimental code and SRU++ implementation are available on the dev branch which will be merged into master later. -
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SSD
A PyTorch Implementation of Single Shot MultiBox Detector
SSD is a PyTorch implementation of the Single Shot MultiBox Detector, a well-known object detection architecture introduced in the original SSD paper. It is built to help users train, evaluate, and experiment with object detection models using PyTorch rather than the original Caffe implementation. The repository includes the major components needed for an object detection workflow, including training scripts, evaluation scripts, demos, and utility modules. It supports commonly used benchmark datasets such as PASCAL VOC and MS COCO, and it also provides scripts to simplify downloading and setting up those datasets. For training visibility, the project includes support for Visdom so users can monitor loss in real time through a browser-based interface. Its structure makes it useful both as a reference implementation for learning SSD and as a base for custom experimentation in detection research or practical computer vision projects. -
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SSD Keras
A Keras port of single shot MultiBox detector
This is a Keras port of the SSD model architecture introduced by Wei Liu et al. in the paper SSD: Single Shot MultiBox Detector. Ports of the trained weights of all the original models are provided below. This implementation is accurate, meaning that both the ported weights and models trained from scratch produce the same mAP values as the respective models of the original Caffe implementation. The main goal of this project is to create an SSD implementation that is well documented for those who are interested in a low-level understanding of the model. The provided tutorials, documentation and detailed comments hopefully make it a bit easier to dig into the code and adapt or build upon the model than with most other implementations out there (Keras or otherwise) that provide little to no documentation and comments. Use one of the provided trained models for transfer learning. -
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SVM# is a svm(support vector machine) classification implemented in C#. The project contains both train and predict modules.
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Sacred
Sacred is a tool to help you configure, andorganize IDSIA experiments
Sacred is a tool to help you configure, organize, log and reproduce experiments. It is designed to do all the tedious overhead work that you need to do around your actual experiment. A very convenient way of the local variables in a function to define the parameters your experiment uses. You can access all parameters of your configuration from every function. They are automatically injected by name. You get a powerful command-line interface for each experiment that you can use to change parameters and run different variants. Observers that log all kinds of information about your experiment, its dependencies, the configuration you used, the machine it is run on, and of course the result. These can be saved to a MongoDB, for easy access later. -
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Savant
Python Computer Vision & Video Analytics Framework With Batteries Incl
Savant is an open-source, high-level framework for building real-time, streaming, highly efficient multimedia AI applications on the Nvidia stack. It helps to develop dynamic, fault-tolerant inference pipelines that utilize the best Nvidia approaches for data center and edge accelerators. Savant is built on DeepStream and provides a high-level abstraction layer for building inference pipelines. It is designed to be easy to use, flexible, and scalable. It is a great choice for building smart CV and video analytics applications for cities, retail, manufacturing, and more. -
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Scattertext 0.2.1
Beautiful visualizations of how language differs among document types
A tool for finding distinguishing terms in corpora and displaying them in an interactive HTML scatter plot. Points corresponding to terms are selectively labeled so that they don't overlap with other labels or points.
