Search Results for "python (scikit-learn)" - Page 5

Showing 391 open source projects for "python (scikit-learn)"

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  • 1
    vyper

    vyper

    Pythonic Smart Contract Language for the EVM

    See Installing Vyper to install vyper. See Tools and Resources for an additional list of framework and tools with vyper support. See Documentation for the documentation and overall design goals of the Vyper language. See Learn.Vyperlang.org for learning Vyper by building a Pokémon game. See try.vyperlang.org to use Vyper in a hosted jupyter environment! There is also an online compiler available you can use to experiment with the language and compile to bytecode and/or IR. While the vyper...
    Downloads: 0 This Week
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  • 2
    bookdown

    bookdown

    Authoring Books and Technical Documents with R Markdown

    A open-source (GPL-3) R package to facilitate writing books and long-form articles/reports with R Markdown. Generate printer-ready books and ebooks from R Markdown documents. A markup language easier to learn than LaTeX, and to write elements such as section headers, lists, quotes, figures, tables, and citations. Multiple choices of output formats: PDF, LaTeX, HTML, EPUB, and Word. Possibility of including dynamic graphics and interactive applications (HTML widgets and Shiny apps) Support for languages other than R, including C/C++, Python, and SQL, etc. ...
    Downloads: 0 This Week
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  • 3
    Agent Reinforcement Trainer

    Agent Reinforcement Trainer

    Train multi-step agents for real-world tasks using GRPO

    Agent Reinforcement Trainer, or ART is an open-source reinforcement learning framework tailored to training large language model agents through experience, making them more reliable and performant on multi-turn, multi-step tasks. Instead of just manually crafting prompts or relying on supervised fine-tuning, ART uses techniques like Group Relative Policy Optimization (GRPO) to let agents learn from environmental feedback and reward signals. The framework is designed to integrate easily with Python applications, abstracting much of the RL infrastructure so developers can train agents without deep RL expertise or heavy infrastructure overhead. ART also supports scalable training patterns, observability tools, and integration with hosted platforms like Weights & Biases, and it provides notebooks that demonstrate training on standard benchmarks and tasks.
    Downloads: 0 This Week
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  • 4
    AIQuant

    AIQuant

    AI-powered platform for quantitative trading

    ai_quant_trade is an AI-powered, one-stop open-source platform for quantitative trading—ranging from learning and simulation to actual trading. It consolidates stock trading knowledge, strategy examples, factor discovery, traditional rules-based strategies, various machine learning and deep learning methods, reinforcement learning, graph neural networks, high-frequency trading, C++ deployment, and Jupyter Notebook examples for practical hands-on use. Stock trading strategies: large models,...
    Downloads: 1 This Week
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  • 5
    Dream Textures

    Dream Textures

    Stable Diffusion built-in to Blender

    Create textures, concept art, background assets, and more with a simple text prompt. Use the 'Seamless' option to create textures that tile perfectly with no visible seam. Texture entire scenes with 'Project Dream Texture' and depth to image. Re-style animations with the Cycles render pass. Run the models on your machine to iterate without slowdowns from a service. Create textures, concept art, and more with text prompts. Learn how to use the various configuration options to get exactly what...
    Downloads: 17 This Week
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  • 6
    Flama

    Flama

    Fire up your models with the flame

    Flama is a python library which establishes a standard framework for development and deployment of APIs with special focus on machine learning (ML). The main aim of the framework is to make ridiculously simple the deployment of ML APIs, simplifying (when possible) the entire process to a single line of code. The library builds on Starlette, and provides an easy-to-learn philosophy to speed up the building of highly performant GraphQL, REST and ML APIs.
    Downloads: 2 This Week
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  • 7
    DGL

    DGL

    Python package built to ease deep learning on graph

    Build your models with PyTorch, TensorFlow or Apache MXNet. Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure. DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others. We are keen to bringing graphs closer to deep learning researchers....
    Downloads: 2 This Week
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  • 8
    Tookie-OSINT

    Tookie-OSINT

    Username OSINT tool for discovering accounts across many websites

    Tookie-OSINT is an open source intelligence tool designed to help security researchers, ethical hackers, and investigators discover online accounts associated with a specific username. It automates the process of searching for usernames across multiple websites, making it easier to identify a person's presence on different platforms. By entering a target username, Tookie-OSINT scans a list of supported sites and checks whether the username exists on those platforms. This approach removes the...
    Downloads: 4 This Week
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  • 9
    ML for Beginners

    ML for Beginners

    12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

    ML-For-Beginners is a structured, project-driven curriculum that teaches foundational machine learning concepts with approachable math and lots of code. Organized as a multi-week course, it mixes short lectures with labs in notebooks so learners practice regression, classification, clustering, and recommendation techniques on real datasets. Each lesson aims to connect the algorithm to a relatable scenario, reinforcing intuition before diving into parameters, metrics, and trade-offs. The...
    Downloads: 2 This Week
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  • 10
    Qwen-Image-Layered

    Qwen-Image-Layered

    Qwen-Image-Layered: Layered Decomposition for Inherent Editablity

    Qwen-Image-Layered is an extension of the Qwen series of multimodal models that introduces layered image understanding, enabling the model to reason about hierarchical visual structures — such as separating foreground, background, objects, and contextual layers within an image. This architecture allows richer semantic interpretation, enabling use cases such as scene decomposition, object-level editing, layered captioning, and more fine-grained multimodal reasoning than with flat image...
    Downloads: 6 This Week
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  • 11
    Hindsight

    Hindsight

    Hindsight: Agent Memory That Learns

    Hindsight is an advanced, open-source memory system for AI agents designed to enable long-term learning, reasoning, and consistency across interactions by treating memory as a first-class component of intelligence rather than a simple retrieval layer. It addresses one of the core limitations of modern AI agents, which is their inability to retain and meaningfully use past experiences over time, by introducing a structured, biomimetic memory architecture inspired by how human memory works....
    Downloads: 3 This Week
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  • 12
    The Data Engineering Handbook

    The Data Engineering Handbook

    Links to everything you'd ever want to learn about data engineering

    The Data Engineering Handbook is a comprehensive, community-curated repository that aggregates essential learning resources for anyone interested in becoming a professional data engineer. Rather than being a code project itself, it’s a learning handbook that links to books, articles, tutorials, community groups, boot camps, and real-world project examples that collectively form a roadmap to mastering data engineering skills. It includes beginner and intermediate boot camps, interview guides,...
    Downloads: 0 This Week
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  • 13
    PennyLane

    PennyLane

    A cross-platform Python library for differentiable programming

    A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy,...
    Downloads: 0 This Week
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  • 14
    Mailu

    Mailu

    Insular email distribution - mail server as Docker images

    Mailu is a simple yet full-featured mail server as a set of Docker images. It is free software (both as in free beer and as in free speech), open to suggestions and external contributions. The project aims at providing people with an easily setup, easily maintained and full-featured mail server while not shipping proprietary software nor unrelated features often found in popular groupware. Security, enforced TLS, DANE, MTA-STS, Letsencrypt!, outgoing DKIM, anti-virus scanner, Snuffleupagus,...
    Downloads: 1 This Week
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  • 15
    Courses (Anthropic)

    Courses (Anthropic)

    Anthropic's educational courses

    Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured...
    Downloads: 2 This Week
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  • 16
    RL with PyTorch

    RL with PyTorch

    Clean, Robust, and Unified PyTorch implementation

    RL with PyTorch is a research-oriented repository that provides implementations of deep reinforcement learning algorithms using the PyTorch framework. The project focuses on helping developers and researchers understand reinforcement learning methods by providing clean and reproducible implementations of well-known algorithms. It includes code for popular deep reinforcement learning techniques such as Deep Q-Networks, policy gradient methods, actor-critic architectures, and other modern RL...
    Downloads: 0 This Week
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  • 17
    Peroxide

    Peroxide

    Rust numeric library with high performance and friendly syntax

    ...This can make it more challenging to learn and use Rust libraries that heavily rely on Rust's unique features and syntax.
    Downloads: 0 This Week
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  • 18
    rudderstack

    rudderstack

    Privacy and Security focused Segment-alternative, in Golang

    Quickly deploy flexible, powerful customer data pipelines, then send the data to your entire stack—without the engineering headache. Our complete toolset makes it easy to level-up your customer data stack. Spare your data engineers the headache. Our 180+ integrations, along with custom webhook sources and destinations, save data teams hundred of hours. Say goodbye to different versions of the truth. Our SDKs track anonymous and known users at the source and reconcile users in your warehouse...
    Downloads: 13 This Week
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  • 19
    Generative AI for Beginners (Version 3)

    Generative AI for Beginners (Version 3)

    21 Lessons, Get Started Building with Generative AI

    Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development practices and UX for AI. It also walks through modern application techniques such as function calling, RAG with vector databases, working with open source models, agents, fine-tuning, and using SLMs. ...
    Downloads: 0 This Week
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  • 20
    Instructor

    Instructor

    Structured outputs for llms

    Instructor is a tool that enables developers to extract structured data from natural language using Large Language Models (LLMs). Integrating with Python's Pydantic library allows users to define desired output structures through type hints, facilitating schema validation and seamless integration with IDEs. Instructor supports various LLM providers, including OpenAI, Anthropic, Litellm, and Cohere, offering flexibility in implementation. Its customizable nature permits the definition of...
    Downloads: 0 This Week
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  • 21
    HAXE

    HAXE

    The cross-platform toolkit

    Haxe is an open source high-level strictly-typed programming language with a fast optimizing cross-compiler. Haxe can build cross-platform applications targeting JavaScript, C++, C#, Java, JVM, Python, Lua, PHP, Flash, and allows access to each platform's native capabilities. Haxe has its own VMs (HashLink and NekoVM) but can also run in interpreted mode. Haxe is useful in a wide variety of domains; games, web, mobile, desktop, command-line and cross-platform APIs. Take a look at who is...
    Downloads: 11 This Week
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  • 22
    Tiny CUDA Neural Networks

    Tiny CUDA Neural Networks

    Lightning fast C++/CUDA neural network framework

    This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning-fast "fully fused" multi-layer perceptron (technical paper), a versatile multiresolution hash encoding (technical paper), as well as support for various other input encodings, losses, and optimizers. We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared...
    Downloads: 4 This Week
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  • 23
    MLE-bench

    MLE-bench

    AI multi-agent framework for automating data-driven R&D workflows

    RD-Agent is an open source AI framework designed to automate research and development workflows in data-driven domains. It uses large language models and multiple collaborating agents to simulate the typical cycle of research, experimentation, and improvement that human data scientists follow. It separates the process into two core phases: a research stage that proposes hypotheses and ideas, and a development stage that implements and evaluates them through code execution and experiments. By...
    Downloads: 1 This Week
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  • 24
    Remarkable for Linux

    Remarkable for Linux

    The Markdown Editor for Linux

    With Live Preview you can see your changes as you make them. There is no need to export first to check your syntax. This is accompanied by synchronized scrolling. Remarkable has Github Flavoured Markdown. This has a simple, easy-to-learn syntax with features like checklists, highlighting, links, images and more. Remarkable allows you to export your files to PDF and HTML from within the app. The HTML code is even prettified and PDFs have a TOC. You can style your markdown documents however...
    Downloads: 1 This Week
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  • 25
    DeepCTR-Torch

    DeepCTR-Torch

    Easy-to-use,Modular and Extendible package of deep-learning models

    DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical...
    Downloads: 1 This Week
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