Showing 232 open source projects for "s-tools"

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  • 1
    ML.NET Samples

    ML.NET Samples

    Samples for ML.NET, an open source and cross-platform machine learning

    ML.NET is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers. In this GitHub repo, we provide samples that will help you get started with ML.NET and how to infuse ML into existing and new .NET apps. We're working on simplifying ML.NET usage with additional technologies that automate the creation of the model for you so you don't need to write the code by yourself to train a model, you simply need to provide your datasets. The...
    Downloads: 1 This Week
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  • 2
    Stable Baselines

    Stable Baselines

    A fork of OpenAI Baselines, implementations of reinforcement learning

    ...These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around which new ideas can be added, and as a tool for comparing a new approach against existing ones. We also hope that the simplicity of these tools will allow beginners to experiment with a more advanced toolset, without being buried in implementation details.
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  • 3
    GluonNLP

    GluonNLP

    NLP made easy

    GluonNLP is a toolkit that helps you solve NLP problems. It provides easy-to-use tools that helps you load the text data, process the text data, and train models. To facilitate both the engineers and researchers, we provide command-line-toolkits for downloading and processing the NLP datasets. Gluon NLP makes it easy to evaluate and train word embeddings. Here are examples to evaluate the pre-trained embeddings included in the Gluon NLP toolkit as well as example scripts for training embeddings on custom datasets. ...
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  • 4
    NLP-Models-Tensorflow

    NLP-Models-Tensorflow

    Gathers machine learning and Tensorflow deep learning models for NLP

    NLP-Models-Tensorflow is a collection of natural language processing model implementations built using the TensorFlow deep learning framework. The repository provides numerous examples of neural network architectures used in modern NLP research and applications, including text classification, language modeling, machine translation, and sentiment analysis. Each model implementation is designed to illustrate how common NLP architectures operate, such as recurrent neural networks, convolutional...
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  • 5
    Forecasting Best Practices

    Forecasting Best Practices

    Time Series Forecasting Best Practices & Examples

    ...Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. ...
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  • 6
    COCO Annotator

    COCO Annotator

    Web-based image segmentation tool for object detection & localization

    ...It provides many distinct features including the ability to label an image segment (or part of a segment), track object instances, label objects with disconnected visible parts, and efficiently store and export annotations in the well-known COCO format. The annotation process is delivered through an intuitive and customizable interface and provides many tools for creating accurate datasets. Several annotation tools are currently available, with most applications as a desktop installation. Once installed, users can manually define regions in an image and creating a textual description. Generally, objects can be marked by a bounding box, either directly, through a masking tool, or by marking points to define the containing area. ...
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  • 7
    SINGA

    SINGA

    A distributed deep learning platform

    ...SINGA supports loading ONNX format models and saving models defined using SINGA APIs into ONNX format, which enables AI developers to use models across different libraries and tools. SINGA supports the time profiling of each of the operators buffered in the graph. Half precision is supported to bring benefits.
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  • 8
    NLP Best Practices

    NLP Best Practices

    Natural Language Processing Best Practices & Examples

    ...The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on problems involving text and language. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in NLP algorithms, neural architectures, and distributed machine learning systems.
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  • 9
    awesome-TS-anomaly-detection

    awesome-TS-anomaly-detection

    List of tools & datasets for anomaly detection on time-series data

    All lists are in alphabetical order. In the lists, maintained projects are prioritized vs not mantained. A repository is considered "not maintained" if the latest commit is > 1 year old, or explicitly mentioned by the authors.
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  • 10
    ChainerRL

    ChainerRL

    ChainerRL is a deep reinforcement learning library

    ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL. ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. Environments that support the subset of OpenAI Gym's interface (reset and step methods) can be used.
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  • 11
    Top deep learning Github repositories

    Top deep learning Github repositories

    Top 200 deep learning Github repositories sorted by stars

    ...The repository categorizes projects related to neural networks, computer vision, natural language processing, reinforcement learning, and other areas of artificial intelligence. By collecting popular open-source implementations in one place, the project simplifies the process of exploring cutting-edge tools and research implementations for deep learning practitioners. The curated lists are particularly helpful for developers who want to quickly identify well-maintained projects with strong community support.
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  • 12
    AI Cheatsheets

    AI Cheatsheets

    Essential Cheat Sheets for deep learning and machine learning research

    cheatsheets-ai is an open-source repository that collects essential cheat sheets covering many tools and concepts used in machine learning, deep learning, and data science. The project aims to provide quick-reference materials that help engineers, researchers, and students review key techniques and frameworks without reading extensive documentation. It compiles cheat sheets for widely used libraries and technologies such as TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, and PySpark. ...
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  • 13
    NiftyNet

    NiftyNet

    An open-source convolutional neural networks platform for research

    ...NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging data. Quickly build new solutions to your own image analysis problems. NiftyNet currently supports medical image segmentation and generative adversarial networks. NiftyNet is not intended for clinical use.
    Downloads: 0 This Week
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  • 14
    ELI5

    ELI5

    A library for debugging/inspecting machine learning classifiers

    ELI5 is a Python library designed to help developers interpret, debug, and explain the predictions of machine learning models. The project focuses on improving model transparency by providing tools that visualize feature importance and prediction reasoning. It supports several popular machine learning frameworks including scikit-learn, XGBoost, LightGBM, CatBoost, and Keras. The library allows users to inspect model weights, analyze decision trees, and compute permutation feature importance for black-box models. It also provides specialized tools such as TextExplainer, which can highlight important words in text classification tasks to explain why a model produced a particular prediction. ...
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  • 15
    AIAlpha

    AIAlpha

    Use unsupervised and supervised learning to predict stocks

    AIAlpha is a machine learning project focused on building predictive models for financial markets and algorithmic trading strategies. The repository explores how artificial intelligence techniques can analyze historical financial data and generate predictions about asset price movements. It provides a research-oriented environment where users can experiment with data processing pipelines, model training workflows, and quantitative trading strategies. The project typically involves collecting...
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  • 16
    lgo

    lgo

    Interactive Go programming with Jupyter

    ...The system supports the full Go language specification and works directly with the standard Go compiler, ensuring compatibility with typical Go development practices. In addition to running code interactively, lgo supports advanced notebook capabilities such as code completion, inspection tools, and rendering of multimedia outputs.
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  • 17
    RoboSat

    RoboSat

    Semantic segmentation on aerial and satellite imagery

    RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars.
    Downloads: 0 This Week
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  • 18
    ChainerCV

    ChainerCV

    ChainerCV: a Library for Deep Learning in Computer Vision

    ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using Chainer. In ChainerCV, we define the object detection task as a problem of, given an image, bounding box-based localization and categorization of objects. Bounding boxes in an image are represented as a two-dimensional array of shape (R,4), where R is the number of bounding boxes and the second axis corresponds to the coordinates of bounding boxes.
    Downloads: 0 This Week
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  • 19
    LibrePhotos

    LibrePhotos

    A self-hosted open source photo management service

    ...The system allows individuals to store and manage their photos and videos locally rather than relying on commercial cloud services. It provides features similar to services like Google Photos but runs on a private server controlled by the user. The application includes AI-powered tools that automatically analyze images to detect faces, objects, and locations, allowing photos to be grouped and searched more efficiently. LibrePhotos supports a wide variety of media formats and provides a web interface that can be accessed from different devices and operating systems. The platform is built using a Django backend and a React frontend, forming a full-stack web application architecture.
    Downloads: 1 This Week
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  • 20
    Easy-TensorFlow

    Easy-TensorFlow

    Simple and comprehensive tutorials in TensorFlow

    The goal of this repository is to provide comprehensive tutorials for TensorFlow while maintaining the simplicity of the code. Each tutorial includes a detailed explanation (written in .ipynb) format, as well as the source code (in .py format). There is a necessity to address the motivations for this project. TensorFlow is one of the deep learning frameworks available with the largest community. This repository is dedicated to suggesting a simple path to learn TensorFlow. In addition to the...
    Downloads: 0 This Week
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  • 21
    Machine Learning Yearning

    Machine Learning Yearning

    Machine Learning Yearning

    ...The point of this book is not to teach traditional machine learning algorithms, but to teach you how to make machine learning algorithms work. Some technical courses in AI will give you a tool, and this book will teach you how to use those tools. If you aspire to be a technical leader in AI and want to learn how to set a direction for your team, this book will help. This book is still a sample draft. In order to make the corresponding Chinese content available to you as soon as possible, the translation time is rushed, and some of the content is inevitably oversight. You can enter the warehouse address through the Github icon in the upper right corner, and make certain modification suggestions.
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  • 22
    LUMINOTH

    LUMINOTH

    Deep Learning toolkit for Computer Vision

    LUMINOTH is an open-source deep learning toolkit designed for computer vision tasks, particularly object detection. The framework is implemented in Python and built on top of TensorFlow and the Sonnet neural network library, providing a modular environment for training and deploying detection models. It was created to simplify the process of building and experimenting with deep learning models capable of identifying objects within images. Luminoth includes support for popular object...
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  • 23
    BossSensor

    BossSensor

    Hide screen when boss is approaching

    ...When the system detects that the trained face appears in the camera view, the program automatically triggers actions such as hiding the user’s screen or switching to a safe display. The software relies on libraries such as OpenCV, TensorFlow, and Python-based machine learning tools to perform face detection and classification. Training the system requires a dataset of labeled images representing the boss and other people so that the model can learn to differentiate between them.
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  • 24
    Pragmatic AI

    Pragmatic AI

    [Book-2019] Pragmatic AI: An Introduction to Cloud-based ML

    Pragmatic AI is the first truly practical guide to solving real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Writing for business professionals, decision-makers, and students who aren’t professional data scientists, Noah Gift demystifies all the tools and technologies you need to get results. He illuminates powerful off-the-shelf cloud-based solutions from Google, Amazon, and Microsoft, as well as accessible techniques using Python and R. ...
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  • 25
    Generative Models

    Generative Models

    Collection of generative models, e.g. GAN, VAE in Pytorch

    This project is a comprehensive open-source collection of implementations of various generative machine learning models designed to help researchers and developers experiment with deep generative techniques. The repository contains practical implementations of well-known architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Restricted Boltzmann Machines, and Helmholtz Machines, implemented primarily using modern deep learning frameworks like PyTorch...
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