Showing 44 open source projects for "+data flow"

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  • 1
    ArkID

    ArkID

    Enterprise IDaaS/IAM platform system

    Rich plug-in, quickly builds an exclusive IDaaS/IAM platform. Easy integration into all your applications. Unified identity, certification, and authority management system. Extendable bottom application architecture based on Plug-in interpolation. You can flexibly and quickly add new functions to the main program without changing the main program. Achieve centralized and safe storage of corporate organizational structure and identity information of massive personnel. Establish a...
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  • 2
    nlpaug

    nlpaug

    Data augmentation for NLP

    This Python library helps you with augmenting nlp for your machine learning projects. Visit this introduction to understand Data Augmentation in NLP. Augmenter is the basic element of augmentation while Flow is a pipeline to orchestra multi augmenters together.
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  • 3
    Machine-Learning

    Machine-Learning

    kNN, decision tree, Bayesian, logistic regression, SVM

    Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying...
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  • 4
    Soma-direct

    Soma-direct

    System Omnichannel Marketing Analysis

    Soma - System Omnichannel Marketing Analysis is an open-source solution for simplifying the processes of analyzing and predicting consumer behavior. With Soma, you can combine data from multiple systems and channels in the profile joining process. Interactions and behaviors registered as a part of a unique user create and show what a Customer Journey looks like. The data is subjected to an analysis process which enables the examination of the significance of individual touchpoints for the...
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  • 5
    BeaEngine 5

    BeaEngine 5

    BeaEngine disasm project

    BeaEngine is a C library designed to decode instructions from 16-bit, 32-bit and 64-bit intel architectures. It includes standard instructions set and instructions set from FPU, MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, VMX, CLMUL, AES, MPX, AVX, AVX2, AVX512 (VEX & EVEX prefixes), CET, BMI1, BMI2, SGX, UINTR, KL, TDX and AMX extensions. If you want to analyze malicious codes and more generally obfuscated codes, BeaEngine sends back a complex structure that describes precisely the...
    Downloads: 1 This Week
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  • 6
    OpenAI Glow

    OpenAI Glow

    Copy code in "Glow: Generative Flow with Invertible 1x1 Convolutions"

    Glow is an open source generative model released by OpenAI that demonstrates flow-based generative modeling techniques. Unlike models that rely on approximate inference, Glow uses invertible transformations to directly learn the data distribution, allowing for exact likelihood computation and efficient sampling. The model is capable of producing high-quality synthetic images while maintaining interpretable latent spaces that enable meaningful manipulation of generated outputs. ...
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  • 7
    Forecasting Best Practices

    Forecasting Best Practices

    Time Series Forecasting Best Practices & Examples

    Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than...
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  • 8
    Aida Lib

    Aida Lib

    Aida is a language agnostic library for text generation

    Aida is a language-agnostic library for text generation. When using Aida, first you compose a tree of operations on your text that includes conditions via branches and other control flow. Later, you fill the tree with data and render the text. A building block is a variable class: Var. Use it to represent a value that you want to control later. A variable can hold numbers (e.g. float, int) or strings. You can create branches and complex logic with Branch. The context, represented by the class Ctx, is useful to create rules that depends on what has been written before. ...
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  • 9
    I3D models trained on Kinetics

    I3D models trained on Kinetics

    Convolutional neural network model for video classification

    ...The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with both RGB and optical flow input streams. The models have achieved state-of-the-art results on benchmark datasets such as UCF101 and HMDB51, and also won first place in the CVPR 2017 Charades Challenge. The project provides TensorFlow and Sonnet-based implementations, pretrained checkpoints, and example scripts for evaluating or fine-tuning models. It also offers sample data, including preprocessed video frames and optical flow arrays, to demonstrate how to run inference and visualize outputs.
    Downloads: 1 This Week
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  • 10
    Pipelines

    Pipelines

    An experimental programming language for data flow

    Pipelines is a language and runtime for crafting massively parallel pipelines. Unlike other languages for defining data flow, the Pipeline language requires the implementation of components to be defined separately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline while providing access to thousands of active libraries for machine learning, data analysis, and processing.
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  • 11

    Autologging

    Easier logging and tracing of Python functions and class methods.

    Autologging eliminates boilerplate logging setup code and tracing code, and provides a means to separate application logging from program flow and data tracing. Autologging provides two decorators and a custom log level: "autologging.logged" decorates a class to create a __log member. By default, the logger is named for the class's containing module and name (e.g. "my.module.ClassName"). "autologging.traced" decorates a class to provide automatic CALL/RETURN tracing for all class, static, and instance methods, as well as the special __init__ method (by default) "autologging.TRACE" is a custom log level (lower than logging.DEBUG) that is registered with the Python logging module when autologging is imported
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  • 12
    Learn Python the Hard Way

    Learn Python the Hard Way

    Concise study notes derived from “Learn Python the Hard Way”

    This repository contains concise study notes derived from “Learn Python the Hard Way,” organized to reinforce core Python concepts through small, targeted examples. It emphasizes hands-on practice—short scripts, exercises, and explanations that help cement syntax, data structures, functions, and modules. The notes call out common gotchas, idioms, and style preferences so learners form good habits early. Because the content is intentionally compact, it’s easy to revisit a topic quickly when...
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  • 13
    Tangent

    Tangent

    Source-to-source debuggable derivatives in pure Python

    Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative code just like the rest of your program. ...
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  • 14
    SSL Logger

    SSL Logger

    Decrypts and logs a process's SSL traffic

    ssl_logger is a Python-based tool that decrypts and logs a target process’s SSL/TLS traffic on Linux and macOS. It attaches to a running process by name or PID and hooks SSL_read and SSL_write calls to capture plaintext data flowing through encrypted connections. Output can be streamed to the console with verbose metadata or written to a PCAP file for later analysis in standard tooling. The utility is powered by dynamic instrumentation using the Frida framework, allowing it to intercept...
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  • 15
    snakeFlow - collector for flow protocol version 5 writetten on python 2.7 By default output data to the console. May store file data and the database MySQL docs (2 parts): http://snakeproject.ru/rubric/article.php?art=python_netflow_collector_1 http://snakeproject.ru/rubric/article.php?art=python_netflow_collector_2
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  • 16
    Infrastructure for creating document flow solutions, document forms, document printing forms. Automated data structure creation and modufication. Basic shems for store managment, accounting and other document flow tasks.
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  • 17

    Hierarchical cluster engine HCE

    Hierarchical Cluster Engine project

    The main idea of this project – to implement the solution that can be used to: construct custom network mesh or distributed network cluster structure with several relations types between nodes, formalize the data flow processing goes from upper node level central source point to down nodes and backward, formalize the management requests handling from multiple source points, support native reducing of multiple nodes results (aggregation, duplicates elimination, sorting and so on), internally support powerful full-text search engine and data storage, provide transactions-less and transactional requests processing, support flexible run-time changes of cluster infrastructure, have many languages bindings for client-side integration APIs in one product build on C++ language... ...
    Downloads: 1 This Week
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  • 18
    Flow Investigation using N-Dimensions
    Flow Investigation using N-Dimensions (FIND) is a program designed for analysis and visualization of Flow Cytometry data. FIND focuses specifically on automated population discovery (clustering) methods. The project targets both users and developers.
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  • 19
    dfShell is a graphical shell in the style of a data flow composition tool. The programs launched by the shell can have >1 inputs and outputs. Backwards compatible with command line programs that use stdin and stdout.
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