Showing 10 open source projects for "implicit"

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    CCXT

    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.
    2.5 out of 5 stars 2 Reviews
    Downloads: 0 This Week
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  • 2
    AWS X-Ray SDK for Python

    AWS X-Ray SDK for Python

    AWS X-Ray SDK for the Python programming language

    AWS X-Ray SDK for the Python programming language. The AWS X-Ray SDK for Python is compatible with Python 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, and 3.9. X-Ray Python SDK will by default generate no-op trace and entity id for unsampled requests and secure random trace and entity id for sampled requests. If customer wants to enable generating secure random trace and entity id for all the (sampled/unsampled) requests (this is applicable for trace id injection into logs use case) then they should set...
    Downloads: 3 This Week
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  • 3
    Selenium-python Helium

    Selenium-python Helium

    Selenium-python but lighter: Helium is the best Python library

    Under the hood, Helium forwards each call to Selenium. The difference is that Helium's API is much more high-level. In Selenium, you need to use HTML IDs, XPaths and CSS selectors to identify web page elements. Helium on the other hand lets you refer to elements by user-visible labels. As a result, Helium scripts are typically 30-50% shorter than similar Selenium scripts. What's more, they are easier to read and more stable with respect to changes in the underlying web page. Selenium-python...
    Downloads: 0 This Week
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  • 4
    UCO3D

    UCO3D

    Uncommon Objects in 3D dataset

    ...It provides about 170,000 full videos per object instance rather than still frames, along with per-video annotations including object masks, calibrated camera poses, and multiple flavors of point clouds. Each sequence also ships with a precomputed 3D Gaussian Splat reconstruction, enabling fast, differentiable rendering workflows and modern implicit/point-based modeling experiments. The repository includes automated downloaders with checksum verification, fine-grained controls to fetch only selected modalities or super-categories, and a lightweight Python API for loading frames, geometry, and splats on demand. Metadata is indexed in SQLite for quick queries at scale, and helper builders handle alignment, undistortion, frame extraction from videos, and cropping around the object.
    Downloads: 0 This Week
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    DAE Tools Project

    DAE Tools Project

    Object-oriented equation-based modelling and optimisation software

    ...It is not a modelling language nor a collection of numerical libraries but rather a higher level structure – an architectural design of interdependent software components providing an API for: - Model development/specification - Activities on developed models, such as simulation, optimisation, sensitivity analysis and parameter estimation - Processing of the results, such as plotting and exporting to various file formats - Report generation - Code generation, co-simulation and model exchange The following class of problems can be solved by DAE Tools: - Initial value problems of implicit form - Index-1 DAE systems - With lumped or distributed parameters - Steady-state or dynamic - Continuous with some elements of event-driven systems
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    Downloads: 264 This Week
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  • 6
    Deep Daze

    Deep Daze

    Simple command line tool for text to image generation

    Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text.
    Downloads: 0 This Week
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  • 7
    YOLOR

    YOLOR

    implementation of paper - You Only Learn One Representation

    ...Its central contribution is the use of implicit knowledge to improve network performance without treating every task as fully separate. It is useful for computer vision researchers and developers studying YOLO-style detectors, representation learning, and high-performance detection systems.
    Downloads: 0 This Week
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  • 8
    Albedo

    Albedo

    A recommender system for discovering GitHub repos

    ...It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings. A reproducible setup and Makefile-driven workflow streamline tasks like spinning up services, loading datasets, training models, and generating candidate lists. Because it’s built around Spark’s scalable primitives, Albedo can experiment on substantial snapshots of GitHub metadata rather than toy corpora. ...
    Downloads: 0 This Week
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  • 9
    Kopf

    Kopf

    A Python framework to write Kubernetes operators

    Kopf —Kubernetes Operator Pythonic Framework, is a framework and a library to make Kubernetes operator's development easier, just in a few lines of Python code. The main goal is to bring the Domain-Driven Design to the infrastructure level, with Kubernetes being an orchestrator/database of the domain objects (custom resources), and the operators containing the domain logic (with no or minimal infrastructure logic).
    Downloads: 0 This Week
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  • 10
    IRPF90
    WARNING: Project moved to http://github.com/scemama/irpf90 IRPF90 is a Fortran90 preprocessor written in Python for programming using the Implicit Reference to Parameters (IRP) method. It simplifies the development of large fortran codes in the field of scientific high performance computing.
    5.0 out of 5 stars 2 Reviews
    Downloads: 0 This Week
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