Search Results for "source code to flowchart" - Page 52
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Showing 2483 open source projects for "source code to flowchart"
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yowsup
The WhatsApp lib
yowsup is a python library that enables building applications that can communicate with WhatsApp users. The project started as the protocol engine behind Wazapp for Meego and OpenWA for BB10. Now as a standalone library it can be used to power any custom WhatsApp client. During maintenance of yowsup, several projects have been spawned in order to support different features that get introduced by WhatsApp. Some of those features are not necessarily exclusive to WhatsApp and therefore it only... -
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...Now Dracos currently already up to version 3.1.5 with the code name "KUNTILANAK WITH REMASTERED".
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pyWhat
Identify emails, IP addresses, and more
pyWhat is a Python-based identification tool designed to figure out “what” a piece of text or file content represents, especially in security and OSINT workflows. Given inputs such as hex strings, URLs, email addresses, IP addresses, credit card numbers, cryptocurrency wallets, or entire .pcap capture files, it scans for structured patterns and tells you what it finds. The tool is recursive: it can traverse files and directories to extract meaningful entities, which is useful when analyzing... -
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igel
Machine learning tool that allows you to train and test models
A delightful machine learning tool that allows you to train/fit, test, and use models without writing code. The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I sometimes needed a tool sometimes, which I could use to fast create a machine learning prototype. Whether to build some proof of concept, create a fast draft model to prove a point or use auto ML. I find myself often stuck writing boilerplate code and thinking too much about... -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Machine Learning Financial Laboratory
MlFinLab helps portfolio managers and traders
MlFinLab is a comprehensive Python library designed to support the development of machine learning strategies in quantitative finance and algorithmic trading. The project provides a large collection of tools that implement techniques from academic research on financial machine learning. It covers the full lifecycle of developing data-driven trading strategies, including data preprocessing, feature engineering, labeling techniques, model training, and performance evaluation. Many of the... -
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IdleX - IDLE Extensions for Python
IDLE Extensions
A collection of extensions for Python's IDLE, the Python IDE built with the tkinter GUI toolkit. -
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Aquila X
Easy build your personal search engine with Aquila Network
Easy build your personal search engine with Aquila Network. Aquila X is the gateway to Aquila Network and it's applications. AquilaX is a smart bookmarking tool. You can keep your bookmarks and search through it's contents. Choose to keep all your data in a local server or in the cloud. This is an open source software and thus is auditable. -
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Nimporter
Compile Nim Extensions for Python On Import
Nimporter allows the seamless import of Nim code into Python projects, enabling the use of Nim's performance and syntax within Python applications. -
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Trax
Deep learning with clear code and speed
Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively used and maintained in the Google Brain team. Run a pre-trained Transformer, create a translator in a few lines of code. Features and resources, API docs, where to talk to us, how to open an issue and more. Walkthrough, how Trax works, how to make new models and train on your own data. Trax includes basic models (like ResNet, LSTM, Transformer) and RL algorithms (like REINFORCE, A2C, PPO). It... -
Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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TensorFlow Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
TensorFlow Examples is a comprehensive repository of example implementations, tutorials, and reference code intended to help newcomers and intermediate learners dive into TensorFlow quickly. It contains both Jupyter notebooks and raw source code, covering a broad range of tasks: from basic machine-learning and neural-network models to more advanced use cases, using both TensorFlow v1 and v2 APIs. For clarity and educational value, each example is accompanied by explanatory comments or markdown cells to illustrate what the code does and why — a design that makes it especially suitable for self-learners or students following along with real data. ... -
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Text-ly
Text.ly - An alternative for Notepad.
...This application is compiled from the Pyinstaller library so don't mind there is a vulnerability or something the antivirus program might show it as malware or trojan this happens with most of the apps compiled from the Pyinstaller library. So No worries There is not any malware or virus in the app. the source code is available on GitHub you can simply checkout it. Thank you ...! -
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pyModSlave
Modbus TCP/RTU Slave Software.
...pyModSlave is based on modbus-tk, pySerial, and PyQt5. The repository has moved to GitHub: https://github.com/elbarbas/pyModSlave. SourceForge will continue to host updated binary releases, but the latest source code is maintained on GitHub. PyPI: https://pypi.org/project/pyModSlave/ Related project: QModMaster https://github.com/elbarbas/QModMaster — a Modbus master application Video demos: - Modbus RTU: https://youtu.be/1get1ELKz_A - Modbus TCP: https://youtu.be/nX_kTQwK2f4 - Session: https://youtu.be/y_9Di7E3y8w - Log: https://youtu.be/Xcb6sj1uzyY -
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Muc_systray x86
A demonstration python animation system tray X86
This project show you the potential of mini meme on your taskbar. That will help to maximize your OC expose promotion and the the way interaction. Full source code accessible. -
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pytorch-fcn
PyTorch Implementation of Fully Convolutional Networks
...It reproduces the influential FCN approach that converts classification networks into dense, pixel-level predictors. The package includes FCN32s, FCN16s, FCN8s, and FCN8s-at-once variants with progressively finer output reconstruction. Training code and a PASCAL VOC example are provided so users can reproduce baseline experiments. The repository reports mean intersection-over-union results alongside the original implementation for comparison. Pretrained model links and validation visualizations support evaluation and demonstrations. It can be installed from source or through the torchfcn package and is primarily useful for research, education, and legacy segmentation baselines. -
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VoiceFixer
General Speech Restoration
VoiceFixer is a machine-learning framework for “speech restoration”: given a degraded or distorted audio recording — with noise, clipping, low sampling rate, reverberation, or other artifacts — it attempts to recover high-fidelity, clean speech. The architecture works in two stages: first an analysis stage that tries to extract “clean” intermediate features from the noisy audio (e.g. removing noise, denoising, dereverberation, upsampling), and then a neural vocoder-based synthesis stage that... -
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Thesa
It is a Platform to connect to tryton (json-rpc) and is based on qt
...Thesa's goal is to be able to combine tryton with Qt / Qml, for special cases such as using the opengl performance of qml2 Requirements: pyside2 5.12 or higher: https://download.qt.io/official_releases/QtForPython/pyside2/ Run: python3 main.py you can find source code in: https://github.com/numaelis/thesa -
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TensorNetwork
A library for easy and efficient manipulation of tensor networks
TensorNetwork is a high-level library for building and contracting tensor networks—graphical factorizations of large tensors that underpin many algorithms in physics and machine learning. It abstracts networks as nodes and edges, then compiles efficient contraction orders across multiple numeric backends so users can focus on model structure rather than index bookkeeping. Common network families (MPS/TT, PEPS, MERA, tree networks) are expressed with concise APIs that encourage... -
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BlooketHack
One of the First Blooket hacks online
First you must download python 3.7. On install you do need to check the "Add to path" option when you can do so. Second you must download the code here on GitHub. Third you need to extract the files from the .zip file you downloaded. Finally, just double click the main.py file. Original code by: kgsensei. Works on most game modes, Gold Quest (Tested - Working) Tower Defense (Tested - Working) Café (Tested - Working) Factory (Tested - Working) Racing (Tested - Working) Classic (Tested -... -
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YOLOR
implementation of paper - You Only Learn One Representation
YOLOR is the implementation of “You Only Learn One Representation,” a unified network approach for learning explicit and implicit knowledge together. The project focuses on object detection while exploring how a shared representation can support multiple tasks. It builds on the YOLO family and related PyTorch detection work, combining practical detector training with a research idea about unified representations. YOLOR includes model configurations, training code, evaluation scripts,... -
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MAGeCK
Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout
MAGeCK2 is here: https://github.com/davidliwei/mageck2 Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout (MAGeCK) is a computational tool to identify important genes from the recent genome-scale CRISPR-Cas9 knockout screens technology. For instructions and documentations, please refer to the wiki page. MAGeCK is developed by Wei Li and Han Xu from Dr. Xiaole Shirley Liu's lab at Dana-Farber Cancer Institute/Harvard School of Public Health, and is maintained by Wei Li lab at... -
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LP CSIC/UAB Apps and Code
Software and Code from Laboratori de Proteòmica CSIC/UAB
Software, Code and Documents from Laboratori de Proteòmica CSIC/UAB ( LP-CSIC/UAB: http://proteomica.uab.cat ) -
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Rescatux
Easy to use Repair distribution for both Gnu/Linux and Windows systems
Rescatux is a GNU/Linux repair cd (and eventually also Windows) but it is not like other rescue disks. Rescatux comes with Rescapp. Rescapp is a nice wizard that will guide you through your rescue and repair tasks. When the wizard is not able to solve your problem you can also enjoy of Rescatux unique support features: * Chat: Open the chat for asking help directly in Rescatux channel. * Share log: After running an option you can share its log (the action registry that it has done) so... -
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Xmaldi
Viewing collections of MALDI spectra and perform PCA and DFA analyses
Xmaldi is an application for viewing collections of MALDI spectra and perform PCA and DFA analyses on them (Python 3 version). For more information, you can have a look at the README.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/xmaldi3/code/ci/default/tree/README.md -
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Ligaturizer
Programming Fonts with Ligatures added
This script copies the ligatures (glyphs and rendering information) from Fira Code into any other TrueType or OpenType font. (Note that the ligatures are scale-corrected, but otherwise copied as is from Fira Code; it doesn't create new ligature graphics based on the font you're modifying.) This repo contains a Fontforge python script that you can use to add the Fira Code ligatures to any font, as well as submodules for some popular coding fonts and another script for ligaturizing all of them... -
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Deep-Learning-with-TensorFlow-book
Open source Deep Learning book, based on TensorFlow
Deep-Learning-with-TensorFlow-book is an open-source deep learning book based on TensorFlow 2.0. It combines theory with practical examples, making it suitable for beginners who want to learn deep learning through hands-on code. The repository includes the PDF book, companion source code, course slides, and notebook-style materials. It covers core machine learning and deep learning concepts through TensorFlow-based implementation.
