Search Results for "//sourceforge.net/projects/shanaencoder/"
Sort By:
Showing 97 open source projects for "//sourceforge.net/projects/shanaencoder/"
View related business solutions-
Unrivaled Embedded Payments Solutions | NMINMI Payments is an embedded payments solution that lets SaaS platforms, Software companies and ISVs integrate, brand, and manage payment acceptance directly within their software—without becoming a PayFac or building complex infrastructure. As a full-stack processor, acquirer, and technology partner, NMI handles onboarding, compliance, and risk so you can stay focused on growth. The modular, white-label platform supports omnichannel payments, from online, mobile and in-app to in-store and unattended. Choose from full-code, low-code, or no-code integration paths and launch in weeks, not months. Built-in risk tools, flexible monetization, and customizable branding help you scale faster while keeping full control of your experience. With NMI’s developer-first tools, sandbox testing, and modern APIs, you can embed payments quickly and confidently.
-
Fully managed relational database service for MySQL, PostgreSQL, and SQL ServerCloud SQL manages your databases so you don't have to, so your business can run without disruption. It automates all your backups, replication, patches, encryption, and storage capacity increases to give your applications the reliability, scalability, and security they need.
-
1
Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
This curated list contains 900 awesome open-source projects with a total of 3.3M stars grouped into 34 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. -
2
Zero to Mastery Machine Learning
All course materials for the Zero to Mastery Machine Learning
...It also includes milestone projects that demonstrate how to build end-to-end machine learning systems using real datasets, including classification and regression tasks. -
3
Google Research: Language
Shared repository for open-sourced projects from the Google AI Lang
Google Research: Language is a shared repository maintained by Google Research that contains open-source projects developed by the Google AI Language team. The repository hosts multiple subprojects related to natural language processing, machine learning, and large-scale language understanding systems. Many of the projects included in the repository correspond to research papers released by Google researchers and provide implementations of new NLP algorithms or experimental frameworks. ... -
4
Metaflow
A framework for real-life data science
Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. -
Outplacement, Executive Coaching and Career Development | CareermindsBy helping to avoid the potential risks of RIFs or layoffs through our global outplacement services, companies can move forward with their goals while preserving their internal culture, employer brand, and bottom lines.
-
5
AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. It includes implementations for a wide range of AI topics such as computer vision, agent systems, federated learning, distributed systems, adversarial attacks, and generative AI. ... -
6
CVPR 2026
Collection of CVPR 2026 Papers and Open Source Projects
CVPR2026-Papers-with-Code is a community-maintained repository that collects research papers and corresponding open-source implementations from the CVPR 2026 conference and related computer vision research. The repository acts as a continuously updated catalog of cutting-edge research across a wide range of topics including computer vision, multimodal AI, generative models, diffusion systems, autonomous driving, medical imaging, and remote sensing. Each entry typically links to the research... -
7
Data Science Articles from CodeCut
Collection of useful data science topics along with articles
...Each topic often includes references to code repositories, demonstrations, and video tutorials that show how the tools can be applied in real projects. The repository is intended to help practitioners stay updated with current best practices and technologies in the field of data science. -
8
Ai-Learn
The artificial intelligence learning roadmap compiles 200 cases
...The project also provides a large collection of practical exercises and case studies that allow learners to apply theoretical knowledge through real projects. According to the repository description, it includes nearly two hundred hands-on AI examples developed through years of teaching experience. -
9
MLRun
Machine Learning automation and tracking
...MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements. 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. -
More Bookings. Better Experience.The all-in-one solution built to help you stay organised and get more bookings with thousands of connections to online travel agencies (OTAs), resellers and suppliers.
-
10
Machine Learning and Data Science Apps
A curated list of applied machine learning and data science notebooks
...Most examples are written in Python and frequently use Jupyter notebooks to present practical implementations and experiments. The project encourages contributions from data scientists and domain experts who want to share applied analytics projects and techniques that address real business challenges. -
11
huggingface_hub
The official Python client for the Huggingface Hub
The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine-learning apps hosted on the Hub. You can also create and share your own models, datasets, and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python. -
12
MLOps Zoomcamp
Free MLOps course from DataTalks.Club
...Students learn to use widely adopted tools such as MLflow, orchestration frameworks, and cloud platforms to manage machine learning pipelines. The curriculum emphasizes hands-on projects so learners gain practical experience building automated ML pipelines and maintaining deployed models. -
13
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). -
14
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 reproduce experiments and understand the inner workings of various algorithms. ... -
15
ProjectLearn.io
A curated list of project tutorials for project-based learning
...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. Technologies covered include widely used tools such as React, Node.js, Flutter, TensorFlow, OpenCV, and other contemporary development platforms. ... -
16
DVC Extension for Visual Studio Code
https://github.com/iterative/vscode-dvc
A Visual Studio Code extension that integrates Data Version Control (DVC) into the development environment, enhancing reproducibility and collaboration for machine learning projects. -
17
Shapash
Explainability and Interpretability to Develop Reliable ML models
...It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in an HTML report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria. -
18
CVPR 2025
Collection of CVPR 2025 papers and open source projects
CVPR 2025 curates accepted CVPR 2025 papers and pairs them with their corresponding code implementations when available, giving researchers and practitioners a fast way to move from reading to reproducing. It organizes entries by topic areas such as detection, segmentation, generative models, 3D vision, multi-modal learning, and efficiency, so you can navigate the year’s output efficiently. Each paper entry typically includes a title, author list, and links to the paper PDF and official or... -
19
AI_Tutorial
A selection of learning materials, search, recommendation, advertising
...The project functions as a centralized knowledge base designed to help engineers and researchers discover tutorials, technical articles, algorithm explanations, and architecture discussions from across the AI ecosystem. Rather than focusing on a single framework or course, the repository collects materials from many sources such as open-source projects, technical blogs, research papers, and industry engineering posts. The curated content includes topics like recommendation systems, search engine architecture, neural networks, graph neural networks, and modern deep learning techniques. The goal of the project is to reduce information fragmentation by organizing valuable AI resources into structured sections that can be explored easily by learners and practitioners. -
20
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. -
21
cuML
RAPIDS Machine Learning Library
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU equivalents. ... -
22
omegaml
MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle
omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud. -
23
Stable Baselines3
PyTorch version of Stable Baselines
...These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details. -
24
Diffgram
Training data (data labeling, annotation, workflow) for all data types
From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. Training Data is the art of supervising machines through data. This... -
25
thorough-pytorch
PyTorch Getting Started Tutorial, read online
...Topics include neural network fundamentals, training procedures, model evaluation, and practical deep learning workflows. By combining structured lessons with programming projects, the repository aims to help learners develop both conceptual understanding and practical implementation skills.
