Search Results for "build" - Page 25
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Showing 1103 open source projects for "build"
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PaddleGAN
PaddlePaddle GAN library, including lots of interesting applications
PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on. PaddleGAN provides developers with high-performance implementation of classic and SOTA Generative Adversarial Networks, and supports developers to quickly build, train and deploy GANs for academic, entertainment, and industrial usage. GAN-Generative Adversarial Network, was praised by "the Father of Convolutional Networks" Yann LeCun (Yang Likun) as [One of the most interesting ideas in the field of computer science in the past decade]. It's the one research area in deep learning that AI researchers are most concerned about. -
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SEO Tool
Seo Tool: AI Autoblogger for ArtikelSchreiber.com and UNAIQUE.net
Seo Tool: AI Autoblogger for https://www.artikelschreiber.com/ and https://www.unaique.net/ Function: Create Mini site based on Config File on your server https://linktr.ee/textgenerator Use a Shared Hosting Server Change the "seo-marketing-tool.conf" config to fit your needs (eg: Create a Mini Site about "Cars" then change the AI Software API Keywords to "car") Start the Tool "python3 seo-marketing-tool.py" Your mini site will be created on your Shared Hosting Server HTML5... -
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Siji-ng
A fixed Siji font
A fixed and maintained Siji iconic bitmap font. -
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Nameko
Python framework for building microservices
...Nameko gives you effortless concurrency by yielding workers when they wait for I/O, leaving you free to handle many requests without the worry of threading. Nameko is compatible with almost any protocol, transport or database. Simply use the built-in extensions, build your own or leverage the community. Nameko includes an implementation of RPC over AMQP. It comprises the @rpc entry point, a proxy for services to talk to other services, and a standalone proxy that non-Nameko clients can use to make RPC calls to a cluster. The HTTP entry point is built on top of werkzeug, and supports all the standard HTTP methods (GET/POST/DELETE/PUT etc). -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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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Battery Life Saver
Battery Life Saver can extend the life span of laptop batteries.
Battery Life Saver can extend the life span of laptop batteries. Using a Laptop Battery continuously by overcharging above 90% or by below 15%, reduces its life span. This light-weight .exe program, Battery Life Saver will alarm, on excess charging or discharging. Set the desired Battery Limit Percentage using the slider, say 50% and allow the Battery Life Saver to give alarm. It automatically detects, charging and battery status. For more advanced options, contact the... -
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suc
Stack usage calculator
Calculates stack consumed by every application call tree using, as a primary source, different files generated during build. Checks completeness of collected data and supports manual input of those that are missing. -
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igel
Machine learning tool that allows you to train and test models
...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 where to start. Therefore, I decided to create this tool. igel is built on top of other ML frameworks. It provides a simple way to use machine learning without writing a single line of code. ... -
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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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Aquila DB
An easy to use Neural Search Engine
...It is dead simple to set up, language-agnostic, and drop in addition to your Machine Learning Applications. Aquila DB, as of current features is a ready solution for Machine Learning engineers and Data scientists to build Neural Information Retrieval applications out of the box with minimal dependencies. -
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Shuup
Open Source Multivendor Marketplace Software
Shuup is an open source e-commerce platform that allows you to build innovative custom marketplaces. Be it a niche single marketplace or a multivendor marketplace; a place to sell products, services or rentals; Shuup can create the custom multi-vendor business solution that you want. -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
SageMaker TensorFlow Serving Container is an a open source project that builds docker images for running TensorFlow Serving on Amazon SageMaker. Some of the build and tests scripts interact with resources in your AWS account. Be sure to set your default AWS credentials and region using aws configure before using these scripts. Amazon SageMaker uses Docker containers to run all training jobs and inference endpoints. The Docker images are built from the Dockerfiles in docker/. The Dockerfiles are grouped based on the version of TensorFlow Serving they support. ... -
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Kashgari
Kashgari is a production-level NLP Transfer learning framework
Kashgari is a simple and powerful NLP Transfer learning framework, build a state-of-art model in 5 minutes for named entity recognition (NER), part-of-speech tagging (PoS), and text classification tasks. -
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RegexAssistant
Regex Windows GUI app to help learn, create,& test Regular Expressions
RegexAssistant is a Regex GUI application to help learn, create, and test Regular Expressions. It's an open source stand alone Windows application. RegexAssistant is great for beginners and intermediate-advanced regex users. -It helps beginners to learn regex by providing examples and token cheat-sheet. -Intermediate-advanced users can use RegexAssistant to test complex expressions. -
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Gooey
Turn Python command line programs into a full GUI application
Gooey is a tool for transforming command line interfaces into beautiful desktop applications. It can be used as the frontend client for any language or program. Whether you've built your application in Java, Node, or Haskell, or you just want to put a pretty interface on an existing tool like FFMPEG, Gooey can be used to create a fast, practically free UI with just a little bit of Python (about 20 lines!). To show how this all fits together, and that it really works for anything, we're... -
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FARM
Fast & easy transfer learning for NLP
...It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. ... -
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Transformer TTS
Implementation of a Transformer based neural network
...This design addresses common autoregressive issues such as repetition, skipped words, and unstable attention, and results in robust, fast synthesis where all frames are predicted in parallel. The repository ships with tooling to build datasets (especially LJSpeech) and create training data, plus scripts to train both the aligner and the TTS model, monitor training with TensorBoard, and resume or reset training runs. -
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tensorflow_template_application
TensorFlow template application for deep learning
...Instead of focusing on a specific algorithm, the project emphasizes software engineering practices that make machine learning systems easier to maintain and extend. The template includes configuration files, scripts, and project structures that help teams build reproducible experiments and production-ready pipelines. It is particularly useful for developers who want to transition from experimental notebooks to structured machine learning applications. By providing a reusable framework, the template reduces the time needed to set up new TensorFlow projects and encourages consistent development practices. -
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Earth Enterprise
Google Earth Enterprise - Open Source
Earth Enterprise is the open source version of Google Earth Enterprise (GEE), a powerful geospatial application suite that enables organizations to build and host custom 3D globes and 2D maps using their own imagery and data. Unlike Google Maps or Google Earth, Earth Enterprise does not include Google’s proprietary imagery but instead provides the tools needed to manage and visualize private geospatial datasets. The system is composed of three main components: Fusion, which processes and integrates imagery, vector, and terrain data into unified map layers; Server, which hosts the resulting globes or maps via Apache or Tornado-based web servers; and Client, which includes the Google Earth Enterprise Client (EC) for 3D visualization and the Google Maps JavaScript API V3 for 2D viewing. ... -
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Hands-on Unsupervised Learning
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
This repo contains the code for the O'Reilly Media, Inc. book "Hands-on Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data" by Ankur A. Patel. Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to the holy grail in AI research, the so-called general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied; this is where unsupervised learning comes in. ... -
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DrQA
Reading Wikipedia to Answer Open-Domain Questions
...The reader is a neural model trained on supervised QA data to estimate start and end positions within a paragraph, and it can be adapted to new domains through fine-tuning or distant supervision. The repository includes scripts to build the Wikipedia index, train the reader, and evaluate end-to-end performance. DrQA popularized a practical recipe for combining IR and neural reading, and it remains a strong baseline for open-domain QA research and production prototypes. -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
...The framework currently integrates some of the best-published architectures and it integrates the most common public datasets for ease of reproducibility. It heavily relies on Pytorch Geometric and Facebook Hydra library thanks for the great work! We aim to build a tool that can be used for benchmarking SOTA models, while also allowing practitioners to efficiently pursue research into point cloud analysis, with the end goal of building models which can be applied to real-life applications. Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. ... -
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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 and diagrams. ...
