Search Results for "dynamicreports-examples" - Page 6
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Showing 799 open source projects for "dynamicreports-examples"
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s3-client
Sample python script to work with Amazon S3
Example Python script to work with S3. -
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Nextcord
A Python wrapper for the Discord API forked from discord.py
A modern, easy-to-use, feature-rich, and async-ready API wrapper for Discord written in Python. -
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GraphRAG
A modular graph-based Retrieval-Augmented Generation (RAG) system
The GraphRAG project is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs. -
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Pythonic Data Structures and Algorithms
Minimal examples of data structures and algorithms in Python
...For students preparing for technical interviews, self-learners brushing up on fundamentals, or developers wanting to understand algorithm internals, this repository provides ready-to-run examples, and can serve as a sandbox to experiment, benchmark, or adapt code. Because it’s in pure Python, it’s easy to read and modify, making it accessible even to those with modest programming experience. The repo helps bridge the gap between theoretical algorithm descriptions and real-world code, giving concrete, working implementations that one can study, debug, or extend. -
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LLaMA 3
The official Meta Llama 3 GitHub site
This repository is the former home for Llama 3 model artifacts and getting-started code, covering pre-trained and instruction-tuned variants across multiple parameter sizes. It introduced the public packaging of weights, licenses, and quickstart examples that helped developers fine-tune or run the models locally and on common serving stacks. As the Llama stack evolved, Meta consolidated repositories and marked this one deprecated, pointing users to newer, centralized hubs for models, utilities, and docs. Even as a deprecated repo, it documents the transition path and preserves references that clarify how Llama 3 releases map into the current ecosystem. ... -
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Exploitarium
A single archive of public exploit PoCs and vulnerability research
Exploitarium is a consolidated archive of public proof-of-concept vulnerability research and exploit writeups. It gathers older standalone research repositories into one place while also adding newer entries as self-contained folders. The project includes tracked examples involving software such as 7-Zip, AnyDesk, c-ares, curl, Discourse, Docker, and other targets. Its purpose is research visibility, vulnerability study, and educational review rather than normal application deployment. Because the repository contains exploit proof-of-concepts, it should only be used in authorized labs, defensive analysis, or coordinated research contexts. ... -
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PyQuil
A Python library for quantum programming using Quil
...However, you can also keep reading below to get started with running your first quantum program. Without installing anything, you can quickly get started with quantum programming by exploring our interactive Jupyter Notebook tutorials and examples. To run them in a preconfigured execution environment on Binder, click the "launch binder" badge at the top of the README or the link here! To learn more about the tutorials and how you can add your own, visit the rigetti/forest-tutorials repository. If you'd rather set everything up locally, or are interested in contributing to pyQuil, continue to the next section for instructions on installing pyQuil and the Forest SDK. -
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spacy-transformers
Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy
...To use transfer learning, you’ll need at least a few annotated examples for what you’re trying to predict. -
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ModernGL
Modern OpenGL binding for Python
ModernGL is a Python wrapper over OpenGL, designed to simplify the creation of high-performance, modern graphics applications. It provides an intuitive API for rendering 2D and 3D graphics, making it accessible to both beginners and experienced developers. ModernGL is suitable for applications such as games, simulations, and data visualizations. -
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Book4_Power-of-Matrix
Book_4_Matrix Power | The Iris Book: From Addition, Subtraction
...The project is designed to help readers progress from basic arithmetic toward machine learning fundamentals by building a strong conceptual understanding of vectors, matrices, and their operations. It combines explanatory text, diagrams, and Python examples to bridge theory and practical computation. The material emphasizes geometric interpretation and visual reasoning, which makes abstract linear algebra topics more accessible to beginners and self-learners. The repository is continuously updated and intended to accompany the broader Visualize-ML learning ecosystem. Overall, it serves as a visually driven mathematical foundation for students preparing for data science and machine learning work. -
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Claude Quickstarts
A collection of projects for building deployable applications
Claude Quickstarts is a curated collection of starter projects and templates that help developers quickly begin building applications with the Claude API, making it easier to leverage Anthropic’s Claude models for real use cases. Each quickstart provides a foundational codebase with preconfigured settings and examples for common deployment scenarios, so developers can focus on customizing functionality instead of bootstrapping infrastructure. The repository includes demos, sample integrations, and instructions to get environments running with minimal setup while handling authentication, API calls, and error handling best practices. Because it’s designed as a learning and prototyping resource, Claude Quickstarts supports exploration of interactive applications, backend services, and workflows that benefit from large language model capabilities. -
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python-zeroconf
A pure python implementation of multicast DNS service discovery
A pure python implementation of multicast DNS service discovery. -
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PyKEEN
A Python library for learning and evaluating knowledge graph embedding
PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-modal information). PyKEEN is a Python package for reproducible, facile knowledge graph embeddings. PyKEEN has a function pykeen.env() that magically prints relevant version information about PyTorch, CUDA, and your operating system that can be used for debugging. If you’re in a Jupyter Notebook, it will be pretty-printed as an HTML table. -
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llms-from-scratch-cn
Build a large language model from 0 only with Python foundation
...Rather than focusing on using pre-trained models through APIs, the project emphasizes understanding the internal mechanisms of modern language models, including tokenization, attention mechanisms, transformer architecture, and training workflows. Through a collection of notebooks, code examples, and translated learning materials, users can explore how to implement components such as multi-head attention, data loaders, and training pipelines using Python and PyTorch. -
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mistletoe
A fast, extensible and spec-compliant Markdown parser in pure Python
mistletoe is a Markdown parser in pure Python, designed to be fast, spec-compliant and fully customizable. Apart from being the fastest CommonMark-compliant Markdown parser implementation in pure Python, mistletoe also supports easy definitions of custom tokens. Parsing Markdown into an abstract syntax tree also allows us to swap out renderers for different output formats, without touching any of the core components. -
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Paul's AutoClicker
A fast auto clicker and macro tool made for windows
You can use this application as an auto clicker, a macro, or both. The program has many features built into it to be very versatile and fit everyone's needs. If you need an auto clicker that will click infinitely fast, this can click as fast as your CPU allows. If you need a clicker with some randomization, hold the mouse button down, and click on a certain spot for x times, it can do that too and more. If you want to be able to still use your PC while something is clicking in the background... -
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CCXT
JavaScript/TypeScript/Python/C#/PHP cryptocurrency trading API
The ccxt library is a collection of available crypto exchanges or exchange classes. Each class implements the public and private API for a particular crypto exchange. All exchanges are derived from the base Exchange class and share a set of common methods. To access a particular exchange from ccxt library you need to create an instance of corresponding exchange class. Supported exchanges are updated frequently and new exchanges are added regularly. -
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Science Plots
Matplotlib styles for scientific plotting
Matplotlib styles for scientific figures. This repo has Matplotlib styles to format your figures for scientific papers, presentations, and theses. -
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RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
...It includes code for popular deep reinforcement learning techniques such as Deep Q-Networks, policy gradient methods, actor-critic architectures, and other modern RL approaches. The repository is structured so that users can easily experiment with different algorithms and training environments. Many examples demonstrate how agents learn to interact with simulated environments through trial and error using reinforcement learning principles. The codebase emphasizes clarity and modular design so that researchers can extend the implementations or use them for experimentation and benchmarking. -
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Data Science Articles from CodeCut
Collection of useful data science topics along with articles
The Data-science repository from CodeCutTech is a curated collection of educational content focused on practical tools and workflows used in modern data science projects. Instead of providing a single software package, the repository aggregates articles, tutorials, and examples covering many topics within the data science ecosystem. The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by data scientists. Each topic often includes references to code repositories, demonstrations, and video tutorials that show how the tools can be applied in real projects. ... -
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ML-NLP
This project is a common knowledge point and code implementation
ML-NLP is a large open-source repository that collects theoretical knowledge, practical explanations, and code examples related to machine learning, deep learning, and natural language processing. The project is designed primarily as a learning resource for algorithm engineers and students preparing for technical interviews in machine learning or NLP roles. It compiles important concepts that frequently appear in machine learning discussions, including neural network architectures, training methods, and common algorithmic techniques. ... -
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Claude Code Hooks Mastery
Master Claude Code Hooks
Claude Code Hooks Mastery is a trending community-centric GitHub repository aimed at helping developers master Claude Code hooks — customizable integration points that let users extend, automate, and augment workflows when using Claude Code, an agentic terminal coding assistant. Although the project itself doesn’t include a single coherent application, it functions as a curated collection of advanced hook examples, best practices, and coding patterns that show how to tailor Claude Code to specific use cases such as automated CI workflows, custom command triggers, and integrations with external tools. The repository is part of a larger ecosystem of Claude Code tooling that enables natural-language-driven coding tasks, and the hooks contained here help users go beyond default behaviors to solve real problems efficiently. -
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OpenMLSys-ZH
Machine Learning Systems: Design and Implementation
...The repository includes scripts or tooling to keep translation synchronized with upstream changes, versioning, and possibly translation metadata (contributors, timestamp). Users can browse or clone the translated documentation to follow along with the original content, deploy examples, or understand system internals in their preferred language. -
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an... -
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openTSNE
Extensible, parallel implementations of t-SNE
openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1], a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2], massive speed improvements [3] [4] [5], enabling t-SNE to scale to millions of data points, and various tricks to improve the global alignment of the resulting...