Search Results for "algorithms framework" - Page 6
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Showing 355 open source projects for "algorithms framework"
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mlr
Machine Learning in R
R does not define a standardized interface for its machine-learning algorithms. Therefore, for any non-trivial experiments, you need to write lengthy, tedious, and error-prone wrappers to call the different algorithms and unify their respective output. {mlr} provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression, and survival analysis along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering. ... -
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Pattern
Web mining module for Python, with tools for scraping
...The framework also includes machine learning algorithms that support classification, clustering, and vector space modeling for text analysis tasks. Another component of the library provides tools for analyzing and visualizing networks, making it useful for studying relationships between entities in large datasets. -
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SGX-Full-OrderBook-Tick-Data-Trading
Providing the solutions for high-frequency trading (HFT) strategies
SGX-Full-OrderBook-Tick-Data-Trading-Strategy is an open-source research project focused on modeling high-frequency financial market behavior using machine learning techniques. The repository analyzes tick-level order book data from the Singapore Exchange and attempts to capture the dynamics of limit order book movements. By extracting features such as order depth ratios and price movement indicators, the system trains machine learning models to predict short-term market changes. Several... -
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DifferenceKit
A fast and flexible O(n) difference algorithm framework
A fast and flexible O(n) difference algorithm framework for Swift collection. The algorithm is optimized based on the Paul Heckel’s algorithm. This is a diffing algorithm developed for Carbon, works stand alone. The algorithm optimized based on the Paul Heckel’s algorithm. See also his paper A technique for isolating differences between files released in 1978. It allows all kind of diffs to be calculated in linear time O(n). RxDataSources and IGListKit are also implemented based on his... -
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EasyNLP
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
EasyNLP is an easy-to-use NLP development and application toolkit in PyTorch, first released inside Alibaba in 2021. It is built with scalable distributed training strategies and supports a comprehensive suite of NLP algorithms for various NLP applications. EasyNLP integrates knowledge distillation and few-shot learning for landing large pre-trained models, together with various popular multi-modality pre-trained models. It provides a unified framework of model training, inference, and deployment for real-world applications. It has powered more than 10 BUs and more than 20 business scenarios within the Alibaba group. ... -
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Model Search
Framework that implements AutoML algorithms
Model Search is an AutoML research system for discovering neural network architectures with minimal human intervention. Instead of hand-crafting models, you define a search space and objectives, then the system explores candidate architectures using controllers and population-based strategies. It supports multiple tasks (such as vision or text) by letting you express reusable building blocks—layers, cells, and topologies—that the search can recombine. Training, evaluation, and promotion of... -
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Acme
A library of reinforcement learning components and agents
Acme is a framework from DeepMind for building scalable and reproducible reinforcement learning agents. It emphasizes modular components, distributed training, and ease of experimentation. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. ... -
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SparrowRecSys
A Deep Learning Recommender System
...SparrowRecSys supports a wide range of state-of-the-art recommendation algorithms, including models for click-through rate prediction and user behavior modeling that are widely used in advertising and content recommendation systems. The system is designed as a modular platform combining technologies such as Spark, TensorFlow, and web server components to represent the full lifecycle of recommendation pipelines. -
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With these packages for Delphi the user can easily create VST plugins or ASIO applications within minutes. The included algorithms for filters and dynamics help to built effects without much knowledge of digital signal processing.
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neurojs
A JavaScript deep learning and reinforcement learning library
neurojs is a JavaScript framework designed to enable deep learning and reinforcement learning directly within web environments. The library provides a full machine learning framework implemented in JavaScript that can run inside browsers or Node.js environments. It focuses particularly on reinforcement learning algorithms, enabling developers to create intelligent agents that learn through interaction with simulated environments. -
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Gluon CV Toolkit
Gluon CV Toolkit
...The model zoo is the one-stop shopping center for many models you are expecting. GluonCV embraces a flexible development pattern while is super easy to optimize and deploy without retaining a heavyweight deep learning framework. -
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Self-Attentive Parser
High-accuracy NLP parser with models for 11 languages
LightAutoML is an automated machine learning (AutoML) framework developed by Sberbank AI Lab, designed to facilitate the development of machine learning models with minimal human intervention. -
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MachineLearningStocks
Using python and scikit-learn to make stock predictions
MachineLearningStocks is a Python-based template project that demonstrates how machine learning can be applied to predicting stock market performance. The project provides a structured workflow that collects financial data, processes features, trains predictive models, and evaluates trading strategies. Using libraries such as pandas and scikit-learn, the repository shows how historical financial indicators can be transformed into machine learning features. The model attempts to predict... -
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BioNetExplorer
Framework for Exploration of Bio-signal Processing DNNs in Wearables
...Furthermore, BioNetExplorer reduces the exploration time by deploying genetic algorithms, such as NSGA-II, SPEA-2, etc. Our framework also enables hardware-aware DNN architecture search by imposing user requirements and hardware constraints (storage, FLOPs, etc.) during the exploration stage, thereby limiting the number of networks explored. In case of usage, please refer to: B. S. Prabakaran, A. Akhtar, S. Rehman, O. -
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surpriver
Find big moving stocks before they move using machine learning
...By applying machine learning techniques to market indicators, the tool attempts to identify patterns in trading behavior that deviate significantly from normal market activity. These anomalies are interpreted as signals that a stock may soon experience a major upward or downward move. The framework includes modules for retrieving market data, computing technical indicators, and applying anomaly detection algorithms to identify unusual patterns. The project is intended as a research tool for quantitative finance experiments and algorithmic trading strategy development. -
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TensorFlow 2.0 Tutorials
TensorFlow 2.x version's Tutorials and Examples
...Each section of the repository includes runnable code and structured experiments designed to illustrate how different architectures and algorithms function in real applications. The tutorials use well-known benchmark datasets such as MNIST, CIFAR, and Fashion-MNIST to demonstrate practical model training and evaluation workflows. -
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WhyLogs Java Library
Profile and monitor your ML data pipeline end-to-end
This is a Java implementation of WhyLogs, with support for Apache Spark integration for large scale datasets. Understanding the properties of data as it moves through applications is essential to keeping your ML/AI pipeline stable and improving your user experience, whether your pipeline is built for production or experimentation. WhyLogs is an open source statistical logging library that allows data science and ML teams to effortlessly profile ML/AI pipelines and applications, producing log... -
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Godot Steering AI Framework
A complete framework for Godot to create beautiful and complex AI
...While an AI agent's next action is based on decision-making and planning algorithms, steering behaviors dictate how it will move from one frame to the next. They use available information and calculate where to move at that moment. Joining these systems together can give sophisticated and graceful movement while also being more efficient than complex pathfinding algorithms like A. This project is a framework for the Godot game engine. -
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NLP-Models-Tensorflow
Gathers machine learning and Tensorflow deep learning models for NLP
NLP-Models-Tensorflow is a collection of natural language processing model implementations built using the TensorFlow deep learning framework. The repository provides numerous examples of neural network architectures used in modern NLP research and applications, including text classification, language modeling, machine translation, and sentiment analysis. Each model implementation is designed to illustrate how common NLP architectures operate, such as recurrent neural networks, convolutional... -
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RL Baselines Zoo
A collection of 100+ pre-trained RL agents using Stable Baselines
RL Baselines Zoo is a comprehensive training framework and collection of pre-trained RL agents using Stable-Baselines3. It offers tools for training, tuning, and evaluating RL algorithms across many standard environments, including MuJoCo, Atari, and robotics simulations. Designed for reproducible RL research and benchmarking, it includes scripts, hyperparameter presets, and best practices for training robust agents. -
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ScaffCC
Compilation and optimization framework for the Scaffold language
ScaffCC is a compiler and scheduler for the Scaffold programing language. It is written using the LLVM open-source infrastructure. It is for the purpose of writing and analyzing code for quantum computing applications. ScaffCC enables researchers to compile quantum applications written in Scaffold to a low-level quantum assembly format (QASM), apply error correction, and generate time and area metrics. It is written to be scalable up to problem sizes in which quantum algorithms outperform... -
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frontend-interview
The strongest front-end interview guide in the universe
This is a review summary project about preparing for front-end interviews that I have summarized, and the project is updated from time to time. This is not only a strategy for job interviews, but also a collection of front-end ers to examine themselves and achieve breakthroughs. I hope that through this guide, everyone can open up their own lines of Ren and Du, and go further on the road to the front. This warehouse uses a large number of diagrams to convey knowledge. The so-called picture... -
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ChainerRL
ChainerRL is a deep reinforcement learning library
ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL. ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. ... -
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GnuTLS
Mirror of official gnutls repository
GnuTLS is an open-source secure communications library designed to implement cryptographic protocols such as TLS, SSL, and DTLS for protecting data transmitted over networks. Developed as part of the GNU Project, it provides a robust framework for building secure client-server applications and services that require encrypted communication channels. The library includes implementations of widely used security standards and supports modern encryption algorithms, authentication mechanisms, and certificate management features. GnuTLS is frequently used in network servers, email systems, VPNs, and embedded systems where developers need reliable and standards-compliant TLS functionality. ...