Search Results for "python accounting source code" - Page 61
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Showing 2743 open source projects for "python accounting source code"
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Microsoft Bot Framework SDK
Tool for building conversation applications
Bot Framework provides the most comprehensive experience for building conversation applications. With the Bot Framework SDK, developers can build bots that converse free-form or with guided interactions including using simple text or rich cards that contain text, images, and action buttons. Developers can model and build sophisticated conversation using their favorite programming languages including C#, JS, Python and Java or using Bot Framework Composer, an open-source, visual authoring... -
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VITS
Conditional Variational Autoencoder with Adversarial Learning
VITS is a foundational research implementation of “VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech,” a well-known neural TTS architecture. Unlike traditional two-stage systems that separately train an acoustic model and a vocoder, VITS trains an end-to-end model that maps text directly to waveform using a conditional variational autoencoder combined with normalizing flows and adversarial training. This architecture enables parallel generation... -
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game-programming-patterns
Source repo for the book
...A Python formatting script transforms the book sources and templates into the published web edition. Watch mode can automatically rebuild pages as source files change. Although active development has largely stopped since the book was completed, the repository remains a valuable educational reference and source archive. -
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deoplete.nvim
Dark powered asynchronous completion framework for neovim/Vim8
deoplete.nvim is an asynchronous completion framework for Neovim and Vim 8. It was designed to provide extensible code-completion behavior while avoiding the blocking feel of older synchronous completion plugins. The framework can display completion candidates through Vim’s built-in completion interface and can be extended with dedicated completion sources. It supports a plugin ecosystem where different languages, tools, and contexts can provide their own candidate sources. deoplete.nvim is... -
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CVE-2021-31166
Remote HTTP.sys use-after-free triggered remotely
This is a proof of concept for CVE-2021-31166 ("HTTP Protocol Stack Remote Code Execution Vulnerability"), a use-after-free dereference in http.sys patched by Microsoft in May 2021. The bug itself happens in http!UlpParseContentCoding where the function has a local LIST_ENTRY and appends an item to it. When it's done, it moves it into the Request structure; but it doesn't NULL out the local list. The issue with that is that an attacker can trigger a code-path that frees every entry of the... -
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I Heart LA
Compilable markdown for linear algebra
I Heart LA is a compilable markdown for math. It can generate working code in your favorite language (C++, Python, MATLAB, more to come) and LaTeX from snippets. -
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Machine Learning Collection
A resource for learning about Machine learning & Deep Learning
A resource for learning about Machine learning & Deep Learning. In this repository, you will find tutorials and projects related to Machine Learning. I try to make the code as clear as possible, and the goal is be to used as a learning resource and a way to look up problems to solve specific problems. For most, I have also done video explanations on YouTube if you want a walkthrough for the code. -
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HomeTabs
HomeTabs project helps you to organize bookmarks for web browsers
HomeTabs project helps you to organize bookmarks for web browsers (like a standart browser's home page, but cooler and more comfortable). Design of HomeTabs was inspiried by Mozilla Firefox startpage, i think this is the best way to organise bookmarks, but history of browsing saved on homepage - is bad idea. GitHub: https://github.com/grildroid/HomeTabs Discord: https://discord.gg/6ZGDgFjDVm -
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vae
Simple vae and cvae from keras
vae is a collection of Keras experiments implementing variational autoencoders and related generative models. It includes simple VAE and conditional VAE examples along with several alternative architectures. Separate scripts explore CelebA image generation, convolutional VAEs, clustering-oriented variants, hyperspherical latent spaces, and vector-quantized autoencoders. The repository includes sample output from a CelebA training run as a visual reference. Its documented environment uses... -
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SRU
Training RNNs as Fast as CNNs
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate the training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU... -
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tensorflow_template_application
TensorFlow template application for deep learning
tensorflow_template_application is a template project that demonstrates how to structure scalable applications built with TensorFlow. The repository provides a standardized architecture that helps developers organize machine learning code into clear components such as data processing, model training, evaluation, and deployment. Instead of focusing on a specific algorithm, the project emphasizes software engineering practices that make machine learning systems easier to maintain and extend.... -
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Nerfies
This is the code for Deformable Neural Radiance Fields
Nerfies demonstrates deformation-aware neural radiance fields that reconstruct and render dynamic, real-world scenes from casual video. Instead of assuming a static world, the method learns a canonical space plus a deformation field that maps changing poses or expressions back to that space during training. This lets the system generate photorealistic novel views of nonrigid subjects—faces, bodies, cloth—while preserving fine detail and consistent lighting. The training pipeline handles... -
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Opyrator
Turns your machine learning code into microservices with web API
Instantly turn your Python functions into production-ready microservices. Deploy and access your services via HTTP API or interactive UI. Seamlessly export your services into portable, shareable, and executable files or Docker images. Opyrator builds on open standards - OpenAPI, JSON Schema, and Python type hints - and is powered by FastAPI, Streamlit, and Pydantic. It cuts out all the pain for productizing and sharing your Python code - or anything you can wrap into a single Python... -
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PyCls
Codebase for Image Classification Research, written in PyTorch
pycls is a focused PyTorch codebase for image classification research that emphasizes reproducibility and strong, transparent baselines. It popularized families like RegNet and supports classic architectures (ResNet, ResNeXt) with clean implementations and consistent training recipes. The repository includes highly tuned schedules, augmentations, and regularization settings that make it straightforward to match reported accuracy without guesswork. Distributed training and mixed precision are... -
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PyG2Plot
Python3 binding Plotting Library
PyG2Plot is a Python 3 binding for AntV G2Plot, an interactive and responsive charting library. It lets Python users create statistical charts through a small amount of code while relying on G2Plot’s grammar-of-graphics foundation. The library is inspired by pyecharts and is designed to make web-based charts available from Python workflows. Users create a Plot instance, set chart options, and render the result as an HTML file, HTML string, notebook preview, or JupyterLab output. It also... -
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Open Grid Analysis and Display System
OpenGrADS provides extensions and interfaces for GrADS.
The Grid Analysis and Display System (GrADS) is an interactive desktop tool for easy access, manipulation, and visualization of earth science data. The OpenGrADS Project seeks to develop advanced interfaces and extensions based on the main GrADS engine. -
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OpenDrop
An open Apple AirDrop implementation written in Python
OpenDrop is a command-line tool that allows sharing files between devices directly over Wi-Fi. Its unique feature is that it is protocol-compatible with Apple AirDrop which allows to share files with Apple devices running iOS and macOS. Currently (and probably also for the foreseeable future), OpenDrop only supports sending to Apple devices that are discoverable by everybody as the default contacts-only mode requires Apple-signed certificates. We support contacts-only devices by using... -
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IOS13-SimulateTouch
iOS Automation Framework iOS Touch Simulation Library
A system-wide touch event simulation library for iOS 11.0 - 14. This library enables you to simulate touch events on iOS 11.0 - 14 with just one line of code! Currently, the repository is mainly for programmers. In the future, I will make it suitable for people who do not understand how to code. -
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speedtest-cli
Command line interface for testing internet bandwidth using speedtest
Command line interface for testing internet bandwidth using speedtest.net. It is not a goal of this application to be a reliable latency reporting tool. Latency reported by this tool should not be relied on as a value indicative of ICMP style latency. It is a relative value used for determining the lowest latency server for performing the actual speed test against. Speedtest CLI brings the trusted technology and global server network behind Speedtest to the command line. Measure internet... -
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deep-learning-for-image-processing
deep learning for image processing including classification
deep-learning-for-image-processing is an extensive educational repository covering practical deep learning methods for computer vision. It organizes implementations, explanations, presentation files, and video lessons around major neural network architectures. The material teaches both model structure and training workflows, with examples built in PyTorch and TensorFlow through Keras. Classification topics range from LeNet and AlexNet to ResNet, EfficientNet, Vision Transformer, Swin... -
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FireCX
Open source OWASP penetration testing tool written in Python 3
FireCX is an open source OWASP penetration testing tool written in Python 3, that can speed up the the process of finding common PHP vulnerabilities in PHP code, i.e. command injection, local/remote file inclusion and SQL injection. -
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Ledger in Go
Command line double-entry accounting program
This is a project to parse and import transactions in a ledger file similar to the Ledger command line tool written in C++. Written by Chris Howey Source code: https://github.com/pedroalbanese/ledger -
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Couler
Unified Interface for Constructing and Managing Workflows
Couler is a system designed for unified machine learning workflow optimization in the cloud. Couler endeavors to provide a unified interface for constructing and optimizing workflows across various workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow. Couler enhances workflow efficiency through features like Autonomous Workflow Construction, Automatic Artifact Caching Mechanisms, Big Workflow Auto Parallelism Optimization, and Automatic Hyperparameters Tuning. -
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MLOps Course
Learn how to design, develop, deploy and iterate on ML apps
The MLOps Course by Goku Mohandas is an open-source curriculum that teaches how to combine machine learning with solid software engineering to build production-grade ML applications. It is structured around the full lifecycle: data pipelines, modeling, experiment tracking, deployment, testing, monitoring, and iteration. The repository itself contains configuration, code examples, and links to accompanying lessons hosted on the Made With ML site, which provide detailed narrative explanations... -
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CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network"
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019). The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph...
