Search Results for "java projects applications"
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Showing 48 open source projects for "java projects applications"
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Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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Outbound sales softwareAdversus is an outbound dialing solution that helps you streamline your call strategies, automate manual processes, and provide valuable insights to improve your outbound workflows and efficiency.
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
...You can use your existing Java expertise as an on-ramp to learn and use machine learning and deep learning. You can use your favorite IDE to build, train, and deploy your models. DJL makes it easy to integrate these models with your Java applications. Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. -
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Useful Java links
A list of useful Java frameworks, libraries, software and hello worlds
...These resources cover many areas of software development, including web frameworks, testing libraries, concurrency tools, build systems, microservices architectures, and development best practices. By grouping links into categorized sections, the repository allows developers to quickly discover relevant technologies and learning materials for building Java applications. The project is maintained as a living reference library that evolves alongside the Java ecosystem as new frameworks and development tools emerge. -
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MLRun
Machine Learning automation and tracking
...In MLRun the assets, metadata, and services (data, functions, jobs, artifacts, models, secrets, etc.) are organized into projects. Projects can be imported/exported as a whole, mapped to git repositories or IDE projects (in PyCharm, VSCode, etc.), which enables versioning, collaboration, and CI/CD. Project access can be restricted to a set of users and roles. -
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Machine Learning and Data Science Apps
A curated list of applied machine learning and data science notebooks
This repository is a large curated collection of machine learning and data science resources focused on real-world industry applications. Instead of being a single software framework, it acts as a knowledge base containing links to practical projects, notebooks, datasets, and libraries that demonstrate how machine learning can be applied across different sectors. The repository organizes resources by industry categories such as finance, healthcare, agriculture, manufacturing, government, and retail, allowing practitioners to explore domain-specific machine learning use cases. ... -
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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YOLOv5
YOLOv5 is the world's most loved vision AI
...YOLOv8 is built on cutting-edge advancements in deep learning and computer vision, offering unparalleled performance in terms of speed and accuracy. Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs. Explore the YOLOv8 Docs, a comprehensive resource designed to help you understand and utilize its features and capabilities. Whether you are a seasoned machine learning practitioner or new to the field, this hub aims to maximize YOLOv8's potential in your projects. -
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AiLearning-Theory-Applying
Quickly get started with AI theory and practical applications
AiLearning-Theory-Applying is a comprehensive educational repository designed to help learners quickly understand artificial intelligence theory and apply it in practical machine learning and deep learning projects. The repository provides extensive tutorials covering mathematical foundations, machine learning algorithms, deep learning concepts, and modern large language model architectures. It includes well-commented notebooks, datasets, and implementation examples that allow learners to... -
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ProjectLearn.io
A curated list of project tutorials for project-based learning
ProjectLearn.io is an open-source repository that aggregates curated tutorials focused on project-based programming education. The project organizes learning resources where users build complete applications from scratch, helping learners acquire practical development experience rather than relying solely on theoretical tutorials. The repository includes projects across multiple domains such as web development, mobile development, machine learning, artificial intelligence, and game development. Each project entry typically links to external tutorials that guide learners through building a working application using modern frameworks and programming languages. ... -
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qxresearch-event-1
Python hands on tutorial with 50+ Python Application
qxresearch-event-1 is an open-source educational repository that provides a collection of lightweight Python applications designed to demonstrate programming concepts and artificial intelligence techniques in simple and accessible examples. The repository contains dozens of small programs, many implemented with minimal lines of code, covering topics such as machine learning, graphical user interfaces, computer vision, and API integration. Each example is designed to illustrate a single... -
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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
The AI workplace management platformBy combining AI workflows, predictive intelligence, and automated insights, OfficeSpace gives leaders a complete view of how their spaces are used and how people work. Facilities, IT, HR, and Real Estate teams use OfficeSpace to optimize space utilization, enhance employee experience, and reduce portfolio costs with precision.
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Computer vision projects
computer vision projects | Fun AI projects related to computer vision
Computer vision projects is an open-source collection of computer vision projects and experiments that demonstrate practical applications of modern AI techniques in image processing, robotics, and real-time visual analysis. The repository includes multiple demonstration systems implemented using languages such as Python and C++, covering topics ranging from object detection to embedded vision systems. -
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OpenCV
Open Source Computer Vision Library
OpenCV (Open Source Computer Vision Library) is a comprehensive open-source library for computer vision, machine learning, and image processing. It enables developers to build real-time vision applications ranging from facial recognition to object tracking. OpenCV supports a wide range of programming languages including C++, Python, and Java, and is optimized for both CPU and GPU operations. -
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Computer Vision in Action
A computer vision closed-loop learning platform
...The repository includes structured code examples, scripts, and notebooks that cover pipeline construction, preprocessing, model inference, and visual output rendering, making it easy for newcomers or intermediate practitioners to adapt patterns to their own projects. It also explores how to combine classical computer vision techniques with modern neural network-based models, offering insight into when each approach is most effective. -
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Smile
Statistical machine intelligence and learning engine
...Smile is a couple of times faster than the closest competitor. The memory usage is also very efficient. If we can train advanced machine learning models on a PC, why buy a cluster? Write applications quickly in Java, Scala, or any JVM languages. Data scientists and developers can speak the same language now! Smile provides hundreds advanced algorithms with clean interface. Scala API also offers high-level operators that make it easy to build machine learning apps. And you can use it interactively from the shell, embedded in Scala. ... -
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CatBoost
High-performance library for gradient boosting on decision trees
CatBoost is a fast, high-performance open source library for gradient boosting on decision trees. It is a machine learning method with plenty of applications, including ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable GPU version, and readily comes with visualization tools. ... -
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Python Code Tutorials
The Python Code Tutorials
Python Code Tutorials is a large educational repository that aggregates programming tutorials from the “The Python Code” website into a structured collection of Python projects and learning materials. The repository covers a wide range of programming topics including cybersecurity, networking, web scraping, machine learning, GUI development, and automation scripts. Each tutorial typically includes complete Python code examples and explanations that demonstrate how to build real tools and applications step by step. ... -
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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... -
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Made With ML
Learn how to develop, deploy and iterate on production-grade ML
Made-With-ML is an open-source educational repository and course designed to teach developers how to build production-grade machine learning systems using modern MLOps practices. The project focuses on bridging the gap between experimental machine learning notebooks and real-world software systems that can be deployed, monitored, and maintained at scale. It provides structured lessons and practical code examples that demonstrate how to design machine learning workflows, manage datasets,... -
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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.... -
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TensorFlow Hub
A library for transfer learning by reusing parts of TensorFlow models
TensorFlow Hub is a repository that provides a library and platform for publishing, discovering, and reusing pre-trained machine learning models built with TensorFlow. The project enables developers to integrate high-quality models into their applications without needing to train them from scratch. Through TensorFlow Hub, researchers and practitioners can share reusable model components such as image classifiers, text embedding models, and object detection networks. These models can be... -
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ADAMS
ADAMS is a workflow engine for building complex knowledge workflows.
ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators. Operators include machine learning (WEKA, MOA, MEKA)... -
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Alink
Alink is the Machine Learning algorithm platform based on Flink
Alink is Alibaba’s scalable machine learning algorithm platform built on Apache Flink, designed for batch and stream data processing. It provides a wide variety of ready-to-use ML algorithms for tasks like classification, regression, clustering, recommendation, and more. Written in Java and Scala, Alink is suitable for enterprise-grade big data applications where performance and scalability are crucial. It supports model training, evaluation, and deployment in real-time environments and integrates seamlessly into Alibaba’s cloud ecosystem. -
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KotlinDL
High-level Deep Learning Framework written in Kotlin
KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning... -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
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Record matching software
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Machine Learning & Deep Learning
machine learning and deep learning tutorials, articles
...The project focuses on helping learners understand machine learning through hands-on coding examples rather than purely theoretical explanations. Each tutorial walks through the process of building and training models for tasks such as image classification, neural network training, and computer vision applications. The repository also includes explanations of how different algorithms function internally, helping readers connect theoretical knowledge with implementation details. Because the tutorials are organized into separate projects, users can easily explore specific topics or technologies within the machine learning ecosystem.
