Search Results for "learning classifier system"
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Showing 1238 open source projects for "learning classifier system"
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Data management solutions for confident marketingVerify, deduplicate, manipulate, and assign records automatically to keep your CRM data accurate, complete, and ready for business.
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Collect! is a highly configurable debt collection softwareThe flexible & scalable debt collection software built to automate your workflow. From startup to enterprise, we have the solution for you.
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learning
A log of things I'm learning
The learning repository by Amit Chaudhary is a continuously updated log of concepts, technologies, and skills related to software engineering and computer science. Rather than being a traditional software library, the repository acts as a structured knowledge base documenting the author’s ongoing learning journey across topics such as programming, system design, machine learning, and generative AI. -
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System Design Primer
Learn how to design large-scale systems
...The repository also contains study guides for short, medium, and long interview timelines, allowing learners to focus on both breadth and depth depending on their preparation needs. In addition, it includes flashcard decks designed to reinforce learning through spaced repetition, making it easier to retain key system design knowledge. -
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System Design
Learn how to design systems and prepare for system design interviews
This project is an open, course-style repository designed to help you learn system design from fundamentals through advanced, interview-ready thinking. It organizes core networking and distributed-systems concepts into a structured path, so you can build intuition before jumping into “design X” exercises. It covers the building blocks that show up in real architectures, such as DNS, load balancing, caching, CDNs, proxies, scalability and availability tradeoffs, and storage patterns, then connects those pieces to larger system goals. ... -
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springcloud-learning
Build microservices with the Spring Cloud ecosystem
springcloud-learning is a hands-on tutorial repository that walks Java developers through building microservices with the Spring Cloud ecosystem. It breaks concepts into small, runnable modules so you can focus on one capability at a time—service discovery, configuration management, gateway routing, fault tolerance, messaging, and observability. The code emphasizes practical integration with common infrastructure like Nacos/Eureka for registry, Nacos/Config Server for configuration,... -
Field Sales+ for MS Dynamics 365 and SalesforceBring Dynamics 365 and Salesforce wherever you go with Resco’s solution. With powerful offline features and reliable data syncing, your team can access CRM data on mobile devices anytime, anywhere. This saves time, cuts errors, and speeds up customer visits.
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Mall Learning
Tutorial and sample-code repository
mall-learning is a tutorial and sample-code repository that explores an entire e-commerce system architecture from backend to frontend. The associated “mall” project (with tens of thousands of stars) is an open-source full-stack e-commerce platform built with Spring Boot, MyBatis, Elasticsearch, RabbitMQ, Redis, MongoDB, MySQL and containerized via Docker. -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior... -
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Quantitative Trading System
A comprehensive quantitative trading system with AI-powered analysis
Quantitative Trading System is a comprehensive quantitative trading platform that integrates artificial intelligence, financial data analysis, and automated strategy execution within a unified software system. The project is designed to provide an end-to-end infrastructure for building and operating algorithmic trading strategies in financial markets. It includes tools for collecting and processing market data from multiple sources, performing statistical and machine learning analysis, and generating trading signals based on quantitative models. ... -
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DeepCamera
Open-Source AI Camera. Empower any camera/CCTV
...The source code is here It leverages Yolov7 as a person detector, FastReID for person feature extraction, Milvus the local vector database for self-supervised learning to identify unseen persons, Labelstudio to host images locally and for further usage such as label data and train your own classifier. It also integrates with Home-Assistant to empower smart homes with AI technology. -
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plexe
Build a machine learning model from a prompt
plexe lets you build machine-learning systems from natural-language prompts, turning plain English goals into working pipelines. You describe what you want—a predictor, a classifier, a forecaster—and the tool plans data ingestion, feature preparation, model training, and evaluation automatically. Under the hood an agent executes the plan step by step, surfacing intermediate results and artifacts so you can inspect or override choices. -
Failed Payment Recovery for Subscription BusinessesFlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
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PyTorch
Open source machine learning framework
...PyTorch can be used as a replacement for Numpy, or as a deep learning research platform that provides optimum flexibility and speed. -
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pomegranate
Fast, flexible and easy to use probabilistic modelling in Python
pomegranate is a library for probabilistic modeling defined by its modular implementation and treatment of all models as the probability distributions they are. The modular implementation allows one to easily drop normal distributions into a mixture model to create a Gaussian mixture model just as easily as dropping a gamma and a Poisson distribution into a mixture model to create a heterogeneous mixture. But that's not all! Because each model is treated as a probability distribution,... -
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Vowpal Wabbit
Machine learning system which pushes the frontier of machine learning
Vowpal Wabbit is a machine learning system that pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state-of-the-art algorithms with performance in mind. ... -
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Cleanlab
The standard data-centric AI package for data quality and ML
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. -
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Volcano
A Cloud Native Batch System (Project under CNCF)
Volcano is a batch system built on Kubernetes. It provides a suite of mechanisms that are commonly required by many classes of batch & elastic workload including machine learning/deep learning, bioinformatics/genomics, and other "big data" applications. These types of applications typically run on generalized domain frameworks like TensorFlow, Spark, Ray, PyTorch, MPI, etc, which Volcano integrates with. -
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dm_control
DeepMind's software stack for physics-based simulation
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics. The MuJoCo Python bindings support three different OpenGL rendering backends: EGL (headless, hardware-accelerated), GLFW (windowed, hardware-accelerated), and OSMesa (purely software-based). -
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RustScan
The Modern Port Scanner
The Modern Port Scanner. Find ports quickly (3 seconds at its fastest). Run scripts through our scripting engine (Python, Lua, Shell supported). Scans all 65k ports in 3 seconds. Full scripting engine support. Automatically pipe results into Nmap, or use our scripts (or write your own) to do whatever you want. Adaptive learning. RustScan improves the more you use it. No bloated machine learning here, just basic maths. The usuals you would expect. IPv6, CIDR, file input and more.... -
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NBA Sports Betting Machine Learning
NBA sports betting using machine learning
NBA-Machine-Learning-Sports-Betting is an open-source Python project that applies machine learning techniques to predict outcomes of National Basketball Association games for analytical and betting-related research. The system gathers historical team statistics and game data spanning multiple seasons, beginning with the 2007–2008 NBA season and continuing through the present. -
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NeuralNote
Audio Plugin for Audio to MIDI transcription using deep learning
NeuralNote is an open-source audio software tool designed to convert recorded audio into MIDI data using modern machine learning techniques. The software functions as an audio plugin that can be used inside digital audio workstations as well as a standalone application for music production and analysis. Its main purpose is to perform audio-to-MIDI transcription, allowing musicians to record a performance and automatically transform it into editable MIDI notes. -
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RuView
Turn WiFi signals into real-time human sensing and spatial awareness.
...The system runs on low-cost hardware such as ESP32 sensor meshes and performs signal processing and machine learning directly at the edge. By learning the RF signature of each environment over time, RuView adapts automatically to different spaces and improves its sensing accuracy. Designed for applications ranging from healthcare monitoring to disaster response, it enables spaces to gain spatial awareness using the radio signals already present in the environment. -
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TabPFN
Foundation Model for Tabular Data
TabPFN is an open-source machine learning system that introduces a foundation model designed specifically for tabular data analysis. The model is based on transformer architectures and implements a prior-data fitted network that can perform supervised learning tasks such as classification and regression with minimal configuration. Unlike many traditional machine learning workflows that require extensive hyperparameter tuning and training cycles, TabPFN is pre-trained to perform inference directly on tabular datasets. ... -
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operating system simple
Operating System Guide Dari 0
operating_system_guide is a curated, beginner-friendly resource aimed at helping aspiring developers learn how to create an operating system from scratch. Maintained by AzkaDev, the guide collects educational materials, links, and code snippets that walk learners through each major component of OS development—from bootloaders to kernel structures and system calls. While still evolving, the guide acts as a roadmap and inspiration hub for those wanting to explore low-level systems programming... -
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Thinc
A refreshing functional take on deep learning
Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose,... -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support,... -
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Assembly programming
Learning assembly for Linux x86_64
...Topics include processor architecture basics, writing and building assembly programs, memory layout concepts such as stack and heap, and interaction with the operating system through system calls. The material is intended for a broad audience, from beginners curious about low-level computing to experienced developers expanding their systems knowledge. The repository emphasizes hands-on examples to reinforce conceptual understanding. Overall, asm functions as a practical learning pathway into assembly language and computer architecture fundamentals. -
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WiFi DensePose
Turn WiFi signals into real-time human pose estimation and detection
WiFi DensePose is a production-oriented implementation of a WiFi-based human pose estimation system that enables real-time full-body tracking using wireless signals rather than cameras. The project demonstrates how commodity mesh routers and signal processing techniques can be leveraged to infer dense human pose information, even through obstacles such as walls. It is designed to showcase the emerging field of RF-based sensing, where machine learning models interpret wireless channel data to reconstruct human movement and posture. ...
