Search Results for "java machine learning library" - Page 5
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Showing 302 open source projects for "java machine learning library"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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KotlinDL
High-level Deep Learning Framework written in Kotlin
KotlinDL is a high-level Deep Learning API written in Kotlin and inspired by Keras. Under the hood, it uses TensorFlow Java API and ONNX Runtime API for Java. KotlinDL offers simple APIs for training deep learning models from scratch, importing existing Keras and ONNX models for inference, and leveraging transfer learning for tailoring existing pre-trained models to your tasks. This project aims to make Deep Learning easier for JVM and Android developers and simplify deploying deep learning... -
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Penetration Testing Tools
A collection of more than 170+ tools, scripts, cheatsheets
Penetration-Testing-Tools is a curated collection of tools, scripts, cheatsheets and reference materials assembled to help security researchers, red-teamers, and students perform hands-on penetration testing across multiple domains. The repository groups resources by discipline — reconnaissance, web application testing, network exploitation, privilege escalation, post-exploitation and reporting — so users can quickly find relevant utilities and walkthroughs. Many entries include short usage... -
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The HaskellR project
The full power of R in Haskell
The HaskellR project provides an environment for efficiently processing data using Haskell or R code, interchangeably. HaskellR allows Haskell functions to seamlessly call R functions and vice versa. It provides the Haskell programmer with the full breadth of existing R libraries and extensions for numerical computation, statistical analysis and machine learning. Optionally, pass in the --nix flag to all commands if you have the Nix package manager installed. Nix can populate a local build... -
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to... -
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Classic HWUT
Software Unit Tests (Language Independent Approach)
Automation of Unit and System Tests. Tests can be implemented in any language and on many platforms. The flexible approach enables the inclusion of many types of tests, such as memory leak checks (using valgrind), coding rule checks, complexity checks, etc. Tests are run by a simple call to hwut in a base directory of a project. In particular for C, HWUT supports make file generation using 'sos' and 'sols' modes. Remote control-able function stubs may be generated using the 'stub' mode.... -
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Gym
Toolkit for developing and comparing reinforcement learning algorithms
Gym by OpenAI is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents, everything from walking to playing games like Pong or Pinball. Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no... -
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PyTorch Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, etc.
TLlib is an open-source and well-documented library for Transfer Learning. It is based on pure PyTorch with high performance and friendly API. Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please... -
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Windows Dev Box Setup Scripts
Scripts to simplify setting up a Windows developer box
Windows Dev Box Setup Scripts is a Microsoft-maintained collection of PowerShell “recipes” and helper scripts that automate and streamline provisioning a Windows developer workstation for many common stacks (desktop .NET/C++, web/Node, machine learning, DevOps, containers, and more). The project uses Chocolatey and Boxstarter under the hood and exposes one-click Boxstarter links and standalone scripts so you can boot a machine, install SDKs, runtimes, tooling, and manage reboots unattended.... -
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Lingua
The most accurate natural language detection library for Java
Its task is simple: It tells you which language some provided textual data is written in. This is very useful as a preprocessing step for linguistic data in natural language processing applications such as text classification and spell checking. Other use cases, for instance, might include routing e-mails to the right geographically located customer service department, based on the e-mails' languages. -
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Augmentor.jl
A fast image augmentation library in Julia for machine learning
A fast library for increasing the number of training images by applying various transformations. Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a... -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
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OpenPrompt
An Open-Source Framework for Prompt-Learning
Prompt-learning is the latest paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modifies the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. OpenPrompt is a library built upon PyTorch and provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. OpenPrompt supports loading PLMs directly from huggingface transformers. In the future, we will also support PLMs implemented by other... -
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Awesome Conformal Prediction
A professionally curated list of awesome Conformal Prediction videos
awesome-conformal-prediction is a curated “awesome list” repository on GitHub collecting high-quality resources related to conformal prediction: tutorials, books, papers, theses, open-source libraries, videos, and other educational material. It is not a software library itself but a directory of resources for those wanting to learn or work with conformal prediction and uncertainty quantification. This exceptional resource is the culmination of my PhD journey in Machine Learning, specializing in Conformal Prediction under the supervision of its creator, Prof. Vladimir Vovk. Since 2015, I have painstakingly gathered these invaluable resources, and upon completing my PhD (my thesis, "Machine Learning for Probabilistic Prediction," can be found in the "Theses" section), I am thrilled to share my expertise with the global machine learning community. ... -
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SageMaker Scikit-Learn Extension
A library of additional estimators and SageMaker tools based on scikit
A library of additional estimators and SageMaker tools based on scikit-learn. This project contains standalone scikit-learn estimators and additional tools to support SageMaker Autopilot. Many of the additional estimators are based on existing scikit-learn estimators. SageMaker Scikit-Learn Extension is a Python module for machine learning built on top of scikit-learn. -
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OpenCV-MinGW-Build
MinGW 32bit and 64bit version of OpenCV
OpenCV-MinGW-Build is a toolkit and set of build scripts that make it easier to compile OpenCV with the MinGW (Minimalist GNU for Windows) toolchain on Windows platforms. OpenCV itself is a widely used open-source computer vision and machine learning library, but building it on Windows with MinGW can be tricky due to compiler and dependency mismatches, configuration quirks, and platform differences. This repository packages automation, patched CMake configs, and step-by-step instructions so you can generate OpenCV binaries that work with MinGW GCC rather than being tied to MSVC (Microsoft Visual C++). ... -
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Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to build customized models for researchers and developers with whole-pipeline support. Built upon highly-optimized runtime libraries including DGL , Graph4NLP has both high running efficiency and great extensibility. The architecture of... -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes... -
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Interpret-Text
State-of-the-art explainers for text-based machine learning models
A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard. Interpret-Text builds on Interpret, an open source python package for training interpretable models and helping to explain blackbox machine learning systems. We have added extensions to support text models. -
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Arxiv Sanity Preserver
Web interface for browsing, search and filtering arxiv submissions
Arxiv Sanity Preserver is a web application for discovering, organizing, and filtering machine-learning research papers from arXiv. It was created to make the large daily flow of new submissions easier for researchers to manage. The backend downloads papers, extracts their text, creates thumbnails, and computes TF-IDF representations for similarity analysis. Users can search papers, find work similar to a selected paper, browse popular submissions, and maintain a personal library. ... -
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TensorRT Pro
C++ library based on tensorrt integration
High-level interface for C++/Python. Simplify the implementation of the custom plugin. And serialization and deserialization have been encapsulated for easier usage. Simplify the compilation of fp32, fp16 and int8 for facilitating the deployment with C++/Python in server or embedded device. Models ready for use also with examples are RetinaFace, Scrfd, YoloV5, YoloX, Arcface, AlphaPose, CenterNet and DeepSORT(C++). -
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A Synthetic Data Generator for producing mixed datasets described by relevant, irrelevant, and redundant features.
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TensorNetwork
A library for easy and efficient manipulation of tensor networks
TensorNetwork is a high-level library for building and contracting tensor networks—graphical factorizations of large tensors that underpin many algorithms in physics and machine learning. It abstracts networks as nodes and edges, then compiles efficient contraction orders across multiple numeric backends so users can focus on model structure rather than index bookkeeping. -
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Pythonidae
Curated decibans of scientific programming resources in Python
Pythonidae is a curated collection of scientific programming resources in Python, designed to support research and development across a wide range of disciplines. The repository organizes tools and libraries into domain-specific categories, including mathematics, statistics, machine learning, artificial intelligence, biology, chemistry, physics, earth sciences, and supercomputing. It also covers practical areas such as build automation, databases, APIs, computer graphics, and utilities, offering a structured reference for both academic and applied work. While the primary focus is on Python, some entries also highlight resources implemented in other languages like Julia, R, Go, and Java. ... -
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Arraymancer
A fast, ergonomic and portable tensor library in Nim
Arraymancer is a tensor and deep learning library for the Nim programming language, designed for high-performance numerical computations and machine learning applications.
