Search Results for "git:/git.code.sf.net/p/docfetcher/code" - Page 57
Sort By:
Showing 2686 open source projects for "git:/git.code.sf.net/p/docfetcher/code"
View related business solutions-
Cycloid: Hybrid Cloud DevOps collaboration platformEnable your developers to do their best work and increase time-to-market speed with a leading DevOps and Hybrid Cloud platform.
-
Assembled is the only unified platform for staffing and managing your human and AI support team.Assembled is the only platform that unifies AI agents and intelligent workforce management to power fast and flexible support operations. Built for scale, we help teams automate over 50% of customer interactions, forecast with 90%+ accuracy, and optimize staffing across in-house and BPO teams. Orchestrate every chat, email, or call, balancing workloads between human and AI agents in real time — without sacrificing quality or control. Trusted by Stripe, Canva, and Robinhood, Assembled transforms support from a cost center into a strategic advantage. Our Workforce and Vendor Management tools connect forecasting, scheduling, and performance for smarter staffing decisions. AI Agents automate conversations across channels with your workflows and brand voice. AI Copilot empowers agents with real-time guidance, suggested replies, and one-click actions for faster, higher-quality resolutions.
-
1
MMdnn
Tools to help users inter-operate among deep learning frameworks
...We implement a universal converter to convert DL models between frameworks, which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. We provide a model collection to help you find some popular models. We provide a model visualizer to display the network architecture more intuitively. We provide some guidelines to help you deploy DL models to another hardware platform. -
2
repo2docker GitHub Action
A GitHub action to build data science environment images
Trigger repo2docker to build a Jupyter enabled Docker image from your GitHub repository and push this image to a Docker registry of your choice. This will automatically attempt to build an environment from configuration files found in your repository. Images generated by this action are automatically tagged with both latest and <SHA> corresponding to the relevant commit SHA on GitHub. Both tags are pushed to the Docker registry specified by the user. If an existing image with the latest tag... -
3
Albedo
A recommender system for discovering GitHub repos
Albedo is an open-source recommender system aimed at helping developers discover GitHub repositories by learning from activity signals. It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings. A reproducible... -
4
ebfformat
An Efficient Binary data Format
...A program called ebftkpy which has a set of utility functions to work with the .ebf files , e.g., viewing the contents and getting a summary, is also provided. The EBF specification is designed to be concise and easy to understand to make it easier for others to write their own code if needed. It is also designed to simplify the programming of input output routines in different programming languages. In a nutshell an EBF file is a collection of data objects. Each data object is specified by a unique name and a single file can have multiple data objects. Each data object is preceded by a meta-data or header which describes the binary data associated with it. ... -
Workload Automation for Global EnterprisesRedwood lets you orchestrate securely and reliably across any application, service or server, in the cloud or on-premise, all inside a single platform.
-
5
Top Deep Learning Projects
A list of popular github projects related to deep learning
...Rather than being a library itself, it serves as a curated roadmap and reference guide for anyone exploring the deep learning ecosystem — from beginners to experienced practitioners. By aggregating high-star projects across frameworks (TensorFlow, PyTorch), tools (computer vision, NLP, reinforcement learning), tutorials, and research code, it helps users quickly discover reputable and well-maintained repositories. This way one can survey state-of-the-art projects, find learning resources, or pick stable libraries for production — without manually sifting through hundreds of repos. The repository is openly licensed under MIT, making it easy to fork, extend, or contribute updates (e.g. adding newer projects or reordering by recent popularity). -
6
pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
...The repository walks users through core concepts such as tensors, autograd, neural network modules, convolutional networks, recurrent networks, and transfer learning. Each section includes runnable code examples that progressively increase in complexity, helping learners build intuition while practicing hands-on implementation. Because the tutorials are concise and practical, the project is widely used in classrooms and self-study environments. Overall, it functions as both a learning curriculum and a quick reference for common PyTorch workflows. -
7
Free Queue Manager
Web based python-flask Queue management system
A web based management system developed for the purpose of easing the process of orgnizing queues and lines. Like many other (QMS)s Queue Management Systems, FQM does provide a basic dashboard to allow the users of the system and customers alike to interact with the system via a basic yet simple user interface . Brief user guide can be found on https://fqms.github.io/images/user_guide.pdf -
8
Brand new cheatsheets and handouts
Matplotlib 3.1 cheat sheet
...It lays out common use cases (plot types, styling, figure configuration, saving/exporting, subplot layout, etc.) in a concise and organized format — often serving as a “cheat sheet” for rapid look-up. For practitioners working on data-heavy projects, dashboards, or research code where plotting is frequent, it helps speed up development by reducing context-switching and documentation navigation overhead. It is especially useful when you know roughly what you want (e.g. “I need a scatter + histogram marginal plot”) but don’t remember the exact Matplotlib call. -
9
...Подробное описание: https://континентсвободы.рф/утилиты/система/licensechecker-легальность-программ.html Списки свободных программ для замены платных https://sourceforge.net/p/licensechecker/wiki/browse_pages/
-
Eurekos LMS - Build a Smarter CustomerEurekos is a purpose-built LMS that engages customers throughout the entire learning journey from pre-sales, to onboarding, and everything after.
-
10
StellarGraph
Machine Learning on Graphs
...StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn. -
11
DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. -
12
SageMaker Containers
Create SageMaker-compatible Docker containers
...You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. If you use a prebuilt SageMaker Docker image for training, this library may already be included. ... -
13
ENAS in PyTorch
PyTorch implementation of "Efficient Neural Architecture Search
ENAS in PyTorch is a PyTorch implementation of Efficient Neural Architecture Search (ENAS), a method that automates the design of neural network architectures through reinforcement learning and parameter sharing. The repository demonstrates how a controller network can explore a large search space and discover high-performing architectures while dramatically reducing the computational cost traditionally associated with neural architecture search. It is primarily intended as a research and... -
14
Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and compare the results. Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. ... -
15
MGFVisor
Visor for mass spectrometry MGF files
Visor for mass spectrometry MGF files (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/mgfvisor3/code/ci/default/tree/README.md -
16
Tensorflow and deep learning
A crash course in six episodes for software developers
Tensorflow and deep learning repository is an educational deep learning crash course designed to help software developers quickly understand and apply machine learning concepts without requiring advanced academic background. It is structured as a series of guided lessons that combine theoretical explanations, practical examples, and runnable code, allowing learners to build intuition while actively experimenting with models. The repository covers core neural network concepts such as weights, biases, activation functions, and gradient descent, as well as more advanced techniques like convolutional networks, recurrent networks, and reinforcement learning. It includes multiple hands-on projects, such as handwritten digit recognition, airplane detection in images, and text generation using recurrent neural networks, which demonstrate how different architectures solve real-world problems. -
17
...Installation Videos! Part 1: http://youtu.be/rnv2VLcG-eI Part 2: http://youtu.be/eFudbMWHNlQ Special thanks to Wells Oliver for the code for downloading Retrosheet files. And the Chadwick project for its Retrosheet tools. https://sourceforge.net/projects/chadwick/?source=recommended
-
18
Think Bayes
Code repository for Think Bayes
ThinkBayes is the code repository accompanying Think Bayes: a book on Bayesian statistics written in a computational style. Instead of heavy focus on continuous mathematics or calculus, the book emphasizes learning Bayesian inference by writing Python programs. The project includes code examples, scripts, and environments that correspond to the chapters of the book. -
19
Python Handout
Turn Python scripts into handouts with Markdown and figures
...It’s particularly aimed at educators, presenters, and researchers who want to make their written material come alive with runnable demonstrations and interactive problem sets without bundling a full web framework. Handout supports embedding executable exercises where learners can type code, run it in place, and receive immediate feedback inline; it also integrates seamlessly with charting libraries so that data visualizations can be interactive rather than static. With customizable styling and extension hooks, authors can tailor the interactive elements to match the look and feel of their content. -
20
EasierMGF
Converts RAW Thermo Files into MGF files
Converts RAW Thermo Files into MGF files (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/easiermgf3/code/ci/default/tree/README.md - Gallardo, Ó., Ovelleiro, D., Gay, M., Carrascal, M., & Abian, J. (2014). A collection of open source applications for mass spectrometry data mining. PROTEOMICS, 14(20), 2275–2279. https://doi.org/10.1002/pmic.201400124 -
21
Reliable Metrics for Generative Models
Code base for the precision, recall, density, and coverage metrics
Reliable Fidelity and Diversity Metrics for Generative Models (ICML 2020). Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated images has been the Fréchet Inception Distance (FID) score. Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those... -
22
KimPeptid
Analyzes Peptides Properties (Python 3 version)
Analyzes Peptides Properties (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/kimpeptid3/code/ci/default/tree/README.md -
23
Python4Proteomics Course
Python course for Proteomics analysis
Python course (in Spanish) for Proteomics analysis using basically Jupyter NoteBooks. 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/p4p/code/ci/default/tree/readme.md -
24
TCellXTalk
TCellXTalk Web-App from LP CSIC/UAB
TCellXTalk is a comprehensive database of experimentally detected phosphorylation, ubiquitination and acetylation sites in human T cells. The web-app at www.TCellXTalk.org makes TCellXTalk accessible from Internet, and enables the in silico prediction of potential co-modified peptides to facilitate their experimental detection, using targeted or directed mass spectrometry, for the study of protein post-translational modification cross-talk. More detailed information on TCellXTalk and... -
25
Kopf
A Python framework to write Kubernetes operators
Kopf —Kubernetes Operator Pythonic Framework, is a framework and a library to make Kubernetes operator's development easier, just in a few lines of Python code. The main goal is to bring the Domain-Driven Design to the infrastructure level, with Kubernetes being an orchestrator/database of the domain objects (custom resources), and the operators containing the domain logic (with no or minimal infrastructure logic).
