Search Results for "dynamicreports-examples" - Page 65
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Showing 3974 open source projects for "dynamicreports-examples"
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Auth0 B2B Essentials: SSO, MFA, and RBAC Built InAuth0's B2B Essentials plan gives you everything you need to ship secure multi-tenant apps. Unlimited orgs, enterprise SSO, RBAC, audit log streaming, and higher auth and API limits included. Add on M2M tokens, enterprise MFA, or additional SSO connections as you scale.
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Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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FEniCS.jl
A scientific machine learning (SciML) wrapper for the FEniCS
FEniCS.jl is a wrapper for the FEniCS library for finite element discretizations of PDEs. This wrapper includes three parts. Installation and direct access to FEniCS via a Conda installation. Alternatively one may use their current FEniCS installation. A low-level development API and provides some functionality to make directly dealing with the library a little bit easier, but still requires knowledge of FEniCS itself. Interfaces have been provided for the main functions and their... -
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Html Menu Generator for Laravel
Html menu generator for Laravel
...There are some packages out there that can help generating menus, but most of them have a messy API or have become victims of feature creep. That's why we decided to create our own modern menu package that has a beautiful API to work with. We have provided full documentation containing lots of examples. In this post I'd like to walk you through it. The spatie/menu package provides a fluent interface to build menus of any size in your php application. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines... -
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bitcoinj
A library for working with Bitcoin
...It can maintain a wallet, send/receive transactions without needing a local copy of Bitcoin Core and has many other advanced features. It's implemented in Java but can be used from any JVM compatible language, examples in Python and JavaScript are included. Highly optimized lightweight simplified payment verification (SPV) mode. In this mode, only a small part of the blockchain is downloaded, making bitcoin suitable for usage on constrained devices like smartphones or cheap virtual private servers. A wallet class with encryption, fee calculation, multi-signing, deterministic key derivation, pluggable coin selection/coin control, extensions support and event listeners that let you stay up to date with changes in your balance. ... -
Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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OpenQASM
Quantum assembly language for extended quantum circuits
OpenQASM is an imperative programming language designed for near-term quantum computing algorithms and applications. Quantum programs are described using the measurement-based quantum circuit model with support for classical feed-forward flow control based on measurement outcomes. OpenQASM presents a parameterized set of physical logic gates and concurrent real-time classical computations. Its main goal is to serve as an intermediate representation for higher-level compilers to communicate... -
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Speech-AI-Forge
Speech-AI-Forge is a project developed around TTS generation model
Speech-AI-Forge is a full-stack project built around modern text-to-speech generation models, providing both an API server and a Gradio-based web UI for interactive use. At its core, it acts as a hub that wires together multiple speech-related capabilities, including TTS, speech-to-text and LLM-based control flows, behind a consistent interface. The system is designed to be deployed in several ways: you can try it online via hosted demos, spin it up in a one-click Colab environment, run it... -
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Size Limit
Calculate the real cost to run your JS app or lib
Size Limit is a JavaScript performance budget tool that measures the real cost of your JavaScript bundle and prevents regressions by enforcing limits in CI. It calculates not just raw bundle size, but also download and execution time under configurable network conditions, giving a more realistic sense of what users experience. The tool is modular: it offers a CLI and multiple plugins (file, webpack, time) plus presets tailored to different use cases, from big single-page apps to small npm... -
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FairChem
FAIR Chemistry's library of machine learning methods for chemistry
FAIRChem is a unified library for machine learning in chemistry and materials, consolidating data, pretrained models, demos, and application code into a single, versioned toolkit. Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations,... -
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JupyterLab LSP
Coding assistance for JupyterLab (code navigation + hover suggestions
Hover over any piece of code; if an underline appears, you can press Ctrl to get a tooltip with function/class signature, module documentation or any other piece of information that the language server provides. Critical errors have red underline, warnings are orange, etc. Hover over the underlined code to see a more detailed message. Use the context menu entry, or Alt + 🖱️ to jump to definitions/references (you can change it to Ctrl/⌘ in settings); use Alt + o to jump back. Place your... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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glider
glider is a forward proxy with multiple protocols support
...Load balancing with the following scheduling algorithm, rr: round robin, ha: high availability, lha: latency based high availability, dh: destination hashing. Rule & priority based forwarder choosing: Config Examples. DNS forwarding server. Force upstream querying by TCP. Association rules between dns and forwarder choosing. Association rules between dns and ipset. dns cache support. Custom dns record. IPSet management (linux kernel version >= 2.6.32). Add ip/cidrs from rule files on startup. Add resolved ips for domains from rule files by dns forwarding server. ... -
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GraphJin
Build NodeJS / GO APIs in 5 minutes not weeks
GraphJin is a magical library that instantly converts simple GraphQL into fast and secure APIs. Works with NodeJS and GO. Supports Postgres, MySQL, Yugabyte, AWS Aurora/RDS and Google Cloud SQL. GraphJin gives you an instant secure and fast GraphQL API without code. Just use a GraphQL query to define your API and GraphJin automagically converts it into a full-featured API. Build your backend APIs 100X faster. Works with NodeJS and GO. Supports several databases, Postgres, MySQL, Yugabyte,... -
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ViZDoom
Doom-based AI research platform for reinforcement learning
ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular. ViZDoom is based on ZDOOM, the most popular modern source-port of DOOM. This means compatibility with a huge range of tools and resources that can be used to create custom scenarios, availability of detailed documentation of the engine and tools and support of Doom community.... -
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Python Outlier Detection
A Python toolbox for scalable outlier detection
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly detection. PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural... -
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Minimal Mistakes Jekyll theme
Jekyll theme for building a personal site, blog, project documentation
...If you don’t have this file you’ll need to copy or create one using the theme’s default _config.yml as a base. Take a moment to look over the configuration file included with the theme. Comments have been added to provide examples and default values for most settings. Bundled as a "theme gem" for easier installation/upgrading. -
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AnalysisAVP
Encode decode, rgb yuv h264 aac flv mp4 rtmp
...The project also explores media container formats like MP4 and FLV, along with streaming protocols such as RTMP and WebRTC, offering a broad understanding of media transmission. It includes practical examples and experimental code that demonstrate real-world implementations such as camera capture, rendering, and encoding workflows. The repository also references various multimedia frameworks and libraries like FFmpeg, SDL2, and x264, helping users understand their integration. It is designed as both a study guide and a reference for developers learning multimedia engineering. ... -
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How-To-Ask-Questions-The-Smart-Way
Correctly propose technical questions and get the answers you want
The How-To-Ask-Questions-The-Smart-Way project is a widely recognized guide that teaches developers how to communicate effectively when seeking help in technical communities. It focuses on improving the quality of questions by emphasizing clarity, context, and effort before asking for assistance. The document explains common mistakes, such as vague descriptions or lack of research, and provides strategies for formulating precise and useful questions. It also highlights the importance of... -
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Companion notebooks for Deep Learning
Jupyter notebooks for the code samples of the book
Companion notebooks for Deep Learning is a collection of Jupyter notebooks that accompany François Chollet’s deep learning curriculum, providing hands-on implementations of key concepts using practical examples. The project covers a wide range of topics, including neural networks, computer vision, natural language processing, and sequence modeling. Each notebook is structured to combine theoretical explanations with executable code, allowing users to experiment and learn interactively. The material is designed to be accessible while still covering advanced topics, making it suitable for both beginners and intermediate practitioners. ... -
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NLP
Open source NLP guide with models, methods, and real use cases
NLP is an open source introductory resource for natural language processing, presented as a continuously updated book hosted on GitHub. It explains how machines process and understand human language, combining theory with practical examples. Its covers core NLP concepts such as text representation, feature extraction, and model evaluation, alongside hands-on implementations using tools like Word2Vec, TF-IDF, and FastText. It also introduces topic modeling with LDA, keyword extraction techniques, and document similarity methods. NLP extends into real-world applications, including sentiment analysis and text classification, helping readers connect concepts to use cases. ... -
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AI Notes
Curated AI engineering notes on LLMs, generative models, and tools
ai-notes is a curated repository of notes focused on the evolving landscape of artificial intelligence, particularly generative models and large language models. It is designed to help software engineers quickly understand modern AI concepts, tools, and developments through structured documentation and research notes. It functions as a living knowledge base composed of numerous markdown files that organize topics such as text generation, image generation, AI infrastructure, and code... -
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Machine learning basics
Plain python implementations of basic machine learning algorithms
Machine learning basics repository is an educational project that provides plain Python implementations of fundamental machine learning algorithms designed to help learners understand how these methods work internally. Instead of relying on external machine learning libraries, the algorithms are implemented from scratch so that users can explore the mathematical logic and computational structure behind each technique. The repository includes notebooks that demonstrate classic algorithms such... -
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LLMs-Zero-to-Hero
From nobody to big model (LLM) hero
LLMs-Zero-to-Hero is an open-source educational project designed to guide learners through the complete process of understanding and building large language models from the ground up. The repository presents a structured learning pathway that begins with fundamental concepts in machine learning and progresses toward advanced topics such as model pre-training, fine-tuning, and deployment. Rather than relying entirely on existing frameworks, the project encourages readers to implement... -
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AI Engineering Transition Path
Research papers and blogs to transition to AI Engineering
...Instead of presenting isolated tutorials, the repository provides a structured pathway that guides engineers through the technical knowledge needed to build and deploy large language model systems. The materials include curated research papers, blog posts, and code examples that explain both theoretical foundations and practical implementation strategies. By consolidating these resources into a single repository, the project helps developers navigate the rapidly expanding AI ecosystem without needing to search through scattered materials. -
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POML
Prompt Orchestration Markup Language
...Traditional prompt engineering often relies on unstructured text, which can become difficult to manage as prompts grow more complex and incorporate dynamic data sources. POML addresses this issue by introducing an HTML-like syntax that allows developers to organize prompts into structured components such as roles, tasks, and examples. This structure enables prompts to be reused, modified, and versioned more easily within complex AI applications. The language also supports integration of multiple data types including documents, tables, and other external inputs that must be incorporated into prompts dynamically. By separating prompt content from presentation logic, POML enables developers to maintain cleaner and more maintainable prompt pipelines. -
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Pathway AI Pipelines
Ready-to-run cloud templates for RAG
Pathway AI Pipelines is a collection of ready-to-deploy AI pipeline templates designed to help developers rapidly build production-grade retrieval-augmented generation and enterprise search applications. The project provides end-to-end examples that connect live data sources to LLM workflows, enabling applications to stay synchronized with continuously changing information. It supports numerous connectors including local files, Google Drive, SharePoint, Kafka, PostgreSQL, and real-time APIs, making it suitable for enterprise data environments. The templates include built-in indexing, vector search, hybrid search, and caching capabilities that remove the need to assemble separate infrastructure components. ... -
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Super comprehensive deep learning notes
Super Comprehensive Deep Learning Notes
...The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural network designs like ResNet, transformers, and object detection algorithms. It’s not just a dry code repository; it includes theoretical explanations alongside hands-on examples, loss function explorations, optimization routines, and full end-to-end experiments on real datasets, making it highly suitable for both self-study and classroom use.
