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Showing 1825 open source projects for "sandbox:/mnt/data/project_plan.pod"
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Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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Securden Privileged Account ManagerDiscover and manage administrator, service, and web app passwords, keys, and identities. Automate management with approval workflows. Centrally control, audit, monitor, and record all access to critical IT assets.
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1
Old Photo Restoration
Bringing Old Photo Back to Life (CVPR 2020 oral)
...Specifically, we train two variational autoencoders (VAEs) to respectively transform old photos and clean photos into two latent spaces. And the translation between these two latent spaces is learned with synthetic paired data. This translation generalizes well to real photos because the domain gap is closed in the compact latent space. Besides, to address multiple degradations mixed in one old photo, we design a global branch with a partial nonlocal block targeting to the structured defects, such as scratches and dust spots. -
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Neuro-comma
Punctuation restoration production-ready model for Russian language
This library was developed with the idea to help us to create punctuation restoration models to memorize trained parameters, data, training visualization, etc. The Library doesn't use any high-level frameworks, such as PyTorch-lightning or Keras, to reduce the level entry threshold. Feel free to fork this repo and edit model or dataset classes for your purposes. Our team always uses the latest version and features of Python. We started with Python 3.9, but realized, that there is no FastAPI image for Python 3.9. ... -
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libpostal
A C library for parsing/normalizing street addresses around the world
A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data. libpostal is a C library for parsing/normalizing street addresses around the world using statistical NLP and open data. The goal of this project is to understand location-based strings in every language, everywhere. Addresses and the locations they represent are essential for any application dealing with maps (place search, transportation, on-demand/delivery services, check-ins, reviews). ... -
4
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. -
Get full visibility and control over your tasks and projects with Wrike.Wrike offers world-class features that empower cross-functional, distributed, or growing teams take their projects from the initial request stage all the way to tracking work progress and reporting results.
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5
Hands-on Unsupervised Learning
Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)
...Unsupervised learning can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow. With the hands-on examples and code provided, you will identify difficult-to-find patterns in data. -
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DynaQ
Innovative text document search. http://dynaq.opendfki.de for details.
The goal of DynaQ is to develop an inquiry system to explore the personal information space, supporting you with the searching paradigm 'orienteering'. DynaQ is a (desktop)search engine with enhanced functionality for file, email and blog search. Look at our GitLab homepage for sourcecode and documentation: http://dynaq.opendfki.de -
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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. The template includes configuration files, scripts, and project structures that help teams build reproducible experiments and production-ready pipelines. ... -
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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... -
9
DrQA
Reading Wikipedia to Answer Open-Domain Questions
...The retriever relies on classic IR features (like TF-IDF and n-gram statistics) to remain lightweight and scalable to millions of documents. 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. -
Network Management Software and Tools for Businesses and Organizations | Auvik NetworksReduce IT headaches and save time with a proven solution for automated network discovery, documentation, and performance monitoring. Choose Auvik because you'll see value in minutes, and stay with us to improve your IT for years to come.
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TensorFlow.js models
Pretrained models for TensorFlow.js
This repository hosts a set of pre-trained models that have been ported to TensorFlow.js. The models are hosted on NPM and unpkg so they can be used in any project out of the box. They can be used directly or used in a transfer learning setting with TensorFlow.js. To find out about APIs for models, look at the README in each of the respective directories. In general, we try to hide tensors so the API can be used by non-machine learning experts. New models should have a test NPM script. You... -
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Universal Data Tool
Collaborate & label any type of data, images, text, or documents etc.
An open-source tool and library for creating and labeling datasets of images, audio, text, documents and video in an open data format. The Universal Data Tool can be used by anyone on your team, no data or programming skills needed. Simplicity without sacrificing any powerful developer features and integrations. Use the Universal Data Tool directly from a web browser or with a Windows, Mac or Linux desktop application. Join a link to a collaborative session and see dataset samples from team members complete in real-time. ... -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
...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. Each base class supports a different data format. -
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This project is a reasoner for the description logic EL+. It computes the concept subsumption hierarchy. It is an OWL 2 EL reasoner.
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14
Duckling
Language, engine, and tooling for testing composable language rules
Duckling is a Haskell library developed by Facebook for parsing and normalizing natural language expressions into structured data. It supports a wide range of entities such as dates, times, durations, distances, temperatures, numbers, and currencies. Designed for use in conversational agents, chatbots, and natural language processing applications, Duckling converts fuzzy user input into a consistent and machine-readable format. It features multi-language support and is widely used in production environments requiring robust entity extraction. -
15
Pwnagotchi
Deep Reinforcement learning instrumenting bettercap for WiFi pwning
Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario... -
16
Denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)
...It uses a causal encoder-decoder architecture with skip connections, optimized with losses defined both in the time domain and frequency domain to better suppress noise while preserving speech. Unlike models that operate on spectrograms alone, this design enables lower latency and coherent waveform output. The implementation includes data augmentation techniques applied to the raw waveforms (e.g. noise mixing, reverberation) to improve model robustness and generalization to diverse noise types. The project supports both offline denoising (batch inference) and live audio processing (e.g. via loopback audio interfaces), making it practical for real-time use in calls or recording. ... -
17
Text Gen
Almost state of art text generation library
...Text gen is a python library that allow you build a custom text generation model with ease. Something sweet built with Tensorflow and Pytorch(coming soon). Load your data, your data must be in a text format. Download the example data from the example folder. Tune your model to know the best optimizer, activation method to use. -
18
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. Instead of focusing only on model training, the course emphasizes best practices like modular code design, CI/CD, containerization, reproducibility, and responsible ML (including monitoring and feedback loops). ... -
19
YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
...In the following ROS package, you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. The YOLO packages have been tested under ROS Noetic and Ubuntu 20.04. We also provide branches that work under ROS Melodic, ROS Foxy and ROS2. Darknet on the CPU is fast (approximately 1.5 seconds on an Intel Core i7-6700HQ CPU @ 2.60GHz × 8) but it's like 500 times faster on GPU! ... -
20
Machine Learning Beginner
Machine Learning Beginner Public Account Works
...It introduces the core vocabulary and the mental map of supervised and unsupervised learning before moving into simple algorithms. The materials prioritize conceptual clarity, then progressively add code to solidify understanding. Step-by-step examples help learners see how data preparation, model training, evaluation, and iteration fit together. Because the scope is intentionally beginner-friendly, it’s an approachable springboard to more advanced resources. Educators also reference it as a compact toolkit for workshops and short intro courses. -
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XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
XLM (Cross-lingual Language Model) is a family of multilingual pretraining methods that align representations across languages to enable strong zero-shot transfer. It popularized objectives like Masked Language Modeling (MLM) across many languages and Translation Language Modeling (TLM) that jointly trains on parallel sentence pairs to tighten cross-lingual alignment. Using a shared subword vocabulary, XLM learns language-agnostic features that work well for classification and sequence... -
22
aiode
Discord bot that plays Spotify tracks and YouTube videos or any URL
...Customize how you want to summon your bot by using a custom prefix or giving your bot a name. Advanced admin commands such as updating and rebooting the bot or cleaning up the database available to bot administrators. Capable scripting sandbox that enables running and storing custom Groovy scripts and modifying command behavior through interceptors. -
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Semantic Segmentation in PyTorch
Semantic segmentation models, datasets & losses implemented in PyTorch
Semantic segmentation models, datasets and losses implemented in PyTorch. PyTorch and Torchvision needs to be installed before running the scripts, together with PIL and opencv for data-preprocessing and tqdm for showing the training progress. PyTorch v1.1 is supported (using the new supported tensoboard); can work with earlier versions, but instead of using tensoboard, use tensoboardX. Poly learning rate, where the learning rate is scaled down linearly from the starting value down to zero during training. Considered as the go-to scheduler for semantic segmentation. ... -
24
ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
...Using deep learning models like CNN and RNN with financial and alternative data, and how to generate synthetic data with Generative Adversarial Networks, as well as training a trading agent using deep reinforcement learning. -
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Keepsake
Version control for machine learning
Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get the data back out using the command-line interface or a notebook.
