Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" - Page 24
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Showing 1086 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
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New Relic provides the most powerful cloud-based observability platform built to help companies create more perfect software.Correlate issues across your stack. Debug and collaborate from your IDE. AI assistance at every step. All in one connected experience - not a maze of charts.
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Easily build robust connections between Salesforce and any platformLike having Postman inside Salesforce! Declarative Webhooks allows users to quickly and easily configure bi-directional integrations between Salesforce and external systems using a point-and-click interface. No coding is required, making it a fast and efficient and as a native solution, Declarative Webhooks seamlessly integrates with Salesforce platform features such as Flow, Process Builder, and Apex. You can also leverage the AI Integration Agent feature to automatically build your integration templates by providing it with links to API documentation.
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fooltrader
Quant framework for stock
Build a standard data schema, and then implement various connectors to import systems you are familiar with for analysis. fooltrader is a quantitative analysis trading system designed using big data technology, including data capture, cleaning, structuring, calculation, display, backtesting and trading. Its goal is to provide a unified framework for the whole market (stock, futures, bonds, foreign exchange, digital currency, macroeconomics, etc.) for research, backtesting, forecasting, and trading. ... -
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Digital Preservation Software Platform (DPSP) consists of a number of open source products such as Xena and Digital Preservation Recorder. This installs and configures the complete platform for preserving digital records - just install and use! NO LONGER MAINTAINED, NO LONGER SUPPORTED
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
The cnn-text-classification-tf repository by Denny Britz is a well-known educational implementation of convolutional neural networks for text classification using TensorFlow, aimed at helping developers and researchers understand how CNNs can be applied to natural language processing tasks. Based loosely on Kim’s influential paper on CNNs for sentence classification, this codebase demonstrates how to preprocess text data, convert words into learned embeddings, and apply multiple convolution filters to extract n-gram features that are then pooled and fed into a classifier. The project includes scripts for training, evaluation, and data handling, making it easy to run experiments on datasets such as movie reviews or other labeled text collections. By breaking down the model into understandable components, it serves as a practical reference for students and practitioners learning how deep learning models handle text beyond traditional bag-of-words approaches. -
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Gin Config
Gin provides a lightweight configuration framework for Python
...It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter values, scoped configurations, and modular references to functions, classes, or instances, resulting in highly composable and dynamic experiment setups. Gin is particularly popular in TensorFlow and PyTorch projects, where researchers and developers need to tune numerous interdependent parameters across models, datasets, optimizers, and training pipelines. -
Turn speech into text using Google AINew customers get $300 in free credits to spend on Speech-to-Text. All customers get 60 minutes for transcribing and analyzing audio free per month, not charged against your credits.
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Nick's Python Toolchest
A heterogeneous bunch of useful Python stuff
Nxpy is a Python library that combines general purpose features with a set of tools more geared towards build automation and source code management. It provides convenient ways to invoke and control interactive and non-interactive commands, context aware backup for files you need to modify, convenience stubs for file like objects, and more. It also contains wrappers for ClearCase, Maven and Subversion, as well as tools to manipulate Maven and Visual Studio configuration files. -
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Leo is an outlining-oriented project manager and browser. With Leo you can organize programs, web sites, URL's, pictures or any other kind of data. Leo is fully scriptable in Python. Leo is 100% pure Python. It requires the Qt gui toolkit.
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Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.
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GDL - GNU Data Language, a free IDL (Interactive Data Language, see http://ittvis.com/idl/) compatible incremental compiler.
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Dominate AI Search ResultsAthenaHQ is a cutting-edge platform for Generative Engine Optimization (GEO), designed to help brands optimize their visibility and performance across AI-driven search platforms like ChatGPT, Google AI, and more.
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TensorFlow-ZH
Chinese version of the official document of TensorFlow
...The repo mirrors the structure of the original English docs: chapters, sections, code examples, API references, and supplementary content like configuration and build guides. It includes additional files like a PDF version (compiled LaTeX/TeX sources), table of contents mappings, and translation metadata to track contributions. Over time, the repo has evolved to stay in sync with upstream changes, providing versioned snapshots of the translated content. -
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Python Basics
Some usefull modules and APIs for Python
Often used project independent code for Python. See Code (https://sourceforge.net/p/pythonbasics/code/) for the files. -
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Zhao
A compilation of "The Princely Party Relationship Network"
zhao is a repository that consolidates research, data, and insights related to Zhao, which is likely an individual’s research collection, notes, or curated resources on deep learning, AI, or computational topics (name and content context suggest specialized study). The project may include code examples, experiment results, references to academic papers, mathematical notes, and supporting scripts to explore specific ML methods, benchmarks, or theoretical findings. -
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MAD-X Python Builder
Build windows executable from Python file. Easily compiler your Python
Build windows executable from your Python file, with areas that requires your program info, description, Icon etc. It is possible to load existing data sets, given they are under corresponding Pickle formats, while saving also generates an Pickle file. Info fields can meet most demands, Windowed or consoled program type. One-file or Folder package, Encryption module, Hidden-Imports, UPX Compatible, Icon, program end-name, Debug mode and more. -
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jsondata
Modular JSON by trees and branches, pointers and patches
The 'jsondata' package provides for the modular in-memory processing of JSON data by trees, branches, pointers, and patches. The main interface classes are: - JSONData - Core for RFC7159 based data structures. Provides modular data components. - JSONDataSerializer - Core for RFC7159 based data persistence. Provides modular data serialization. - JSONPointer - RFC6901 for addressing by pointer paths. Provides pointer arithmetics -
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GDeps
Automatic process to update and build your external libraries/projects
...Launch a project script and GDeps will: 1) Create and Update the repository (svn, git, mercurial) 2) Make the solution (cmake, boost, bakefile) 3) Build the solution (Compilers : Mingw, VC90~140, Clang, Digital Mars, Ides : Visual Studio, Codeblocks ). Install: ========== 1) Install Python3: https://www.python.org/ 2) Install GDeps: https://sourceforge.net/projects/gdeps/files/latest/download?source=files 3) Download some samples projects: https://sourceforge.net/projects/gdeps/files/Projects/ 4) Configure your directories: https://sourceforge.net/projects/gdeps/files/Projects/directories.cfg/download 5) Configure your workspace: https://sourceforge.net/projects/gdeps/files/Projects/Config.cfg/download Links: ========== API: http://gdeps.org/doc/index.html Forum: http://gdeps.org/forum/index.php -
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iSCSIsim is a powerful generic test tool for iSCSI targets. Its capabilities include SCSI command injection, error insertion, and recovery testing: all with built-in validation of test results.
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pyhanlp
Chinese participle
...It is commonly used for Chinese-language NLP tasks where you want production-grade tokenization and linguistic analysis, but still want the convenience of Python scripting. The project focuses on making HanLP’s capabilities accessible through a Python-friendly API surface, so you can integrate NLP steps into data pipelines, notebooks, and downstream ML or information-extraction code. In practice, it serves as a bridge layer: Python calls are translated into the corresponding HanLP operations, so you can keep your application logic in Python while relying on HanLP’s implementations. It is especially useful when you need a pragmatic “get results quickly” NLP layer for segmentation, tagging, entity extraction, parsing, or keyword-style tasks rather than experimenting with model training from scratch. -
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Assorted projects. General-purpose libraries for Python, C++, Scala, bash, and others. Meta-programming tools. System utilities. UI components. Web APIs. Configuration files. Benchmarks. Programming competition entries. And much more.
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XOS test builds
AOSP-based Operating System (On phone tap on "Try to download anyway")
halogenOS test builds (release builds are just a leftover) The test builds and modules are supposed to be only used by our testers. But if you are not a tester, and use these builds anyways, don't expect support if you get problems. You can report the bugs, but you have to be experienced enough to know how to revert back to a working state and how to report bugs and provide logs. -
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coldev
C/C++ , PHP, PYTHON, 3D ENGINES, CHROME NATIVE CLIENT
+ C/C++ and PHP projects, Python and Others + 3D ENGINES + Videogames Projects based in Bennugd http://www.bennugd.org Web Page https://coldev.sourceforge.io/ -
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pivottablejs
Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook
PivotTable.js is a Javascript Pivot Table and Pivot Chart library with drag-drop interactivity, and it can now be used with Jupyter/IPython Notebook via the pivottablejs module. I first built PivotTable.js with a plan to build an in-browser data analysis tool, and got as far as one where you could load up a CSV file in the browser for display. Since then, however, the Jupyter project has gathered steam and now provides a browser-based interface to some of the most powerful data processing libraries in the world, so it makes sense to interface with it. -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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...With CordovaGui you can use html, css and javascript preprocessors in easy way. Every time you debug, build or open the index.html file in your browser using CordovaGui, your source files are compiled immediately.
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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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Rekall
Rekall Memory Forensic Framework
Rekall is a powerful memory forensics framework that turns raw RAM captures—or live system state—into structured artifacts investigators can query and script. It ships with a large collection of plugins that parse OS internals to recover processes, modules, sockets, registry hives, and file objects, even when rootkits try to hide them. The design emphasizes repeatability: investigators run well-defined analyses that produce timelines, indicators, and reports suitable for case work or...
