Search Results for "java project with source code" - Page 2
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Showing 76 open source projects for "java project with source code"
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Lucid
A collection of infrastructure and tools for research
Lucid is a collection of infrastructure and tools for research in neural network interpretability. Lucid is research code, not production code. We provide no guarantee it will work for your use case. Lucid is maintained by volunteers who are unable to provide significant technical support. Start visualizing neural networks with no setup. The following notebooks run right from your browser, thanks to Collaboratory. It's a Jupyter notebook environment that requires no setup to use and runs... -
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TensorLayer
Deep learning and reinforcement learning library for scientists
...It provides an extensive collection of customizable neural layers to build advanced AI models quickly, based on this, the community open-sourced mass tutorials and applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. This project can also be found at OpenI and Gitee. 3.0.0 has been pre-released, the current version supports TensorFlow, MindSpore and PaddlePaddle (partial) as the backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. In the future, it will support TensorFlow, MindSpore, PaddlePaddle, PyTorch and other backends. ... -
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ModelDB
Open Source ML Model Versioning, Metadata, and Experiment Management
An open-source system for Machine Learning model versioning, metadata, and experiment management. ModelDB is an open-source system to version machine learning models including their ingredients code, data, config, and environment and to track ML metadata across the model lifecycle. -
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A.I. Stock Trends With WEKA & TA-Lib
A Repository Of The Java Programs Presented in the Videos.
This is the open/public source code repository for the Java programs shown in the YouTube videos - A.I. Stock Trends With WEKA, TA-Lib and more https://www.youtube.com/channel/UCPxmgFZDS7F06UBBxH5b4mg -
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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Dive-into-DL-TensorFlow2.0
Dive into Deep Learning
This project changes the MXNet code implementation in the original book "Learning Deep Learning by Hand" to TensorFlow2 implementation. After consulting Mr. Li Mu by the tutor of archersama , the implementation of this project has been agreed by Mr. Li Mu. Original authors: Aston Zhang, Li Mu, Zachary C. Lipton, Alexander J. Smola and other community contributors. There are some differences between the Chinese and English versions of this book . This project mainly focuses on TensorFlow2... -
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Coursera Machine Learning
Coursera Machine Learning By Prof. Andrew Ng
CourseraMachineLearning is a personal collection of resources, notes, and programming exercises from Andrew Ng’s popular Machine Learning course on Coursera. It consolidates lecture references, programming tutorials, test cases, and supporting materials into one repository for easier review and practice. The project highlights fundamental machine learning concepts such as hypothesis functions, cost functions, gradient descent, bias-variance tradeoffs, and regression models. It also organizes... -
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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. ... -
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SSD Keras
A Keras port of single shot MultiBox detector
This is a Keras port of the SSD model architecture introduced by Wei Liu et al. in the paper SSD: Single Shot MultiBox Detector. Ports of the trained weights of all the original models are provided below. This implementation is accurate, meaning that both the ported weights and models trained from scratch produce the same mAP values as the respective models of the original Caffe implementation. The main goal of this project is to create an SSD implementation that is well documented for those... -
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The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners. Obtaining the teachingbox: FOR USERS: If you want to download the latest releases, please visit: http://search.maven.org/#search|ga|1|teachingbox FOR DEVELOPERS: 1) If you use Apache Maven, just add the following dependency to your pom.xml: <dependency> <groupId>org.sf.teachingbox</groupId> <artifactId>teachingbox-core</artifactId> <version>1.2.3</version> </dependency> 2) If you want to check out the most recent source-code: git clone https://git.code.sf.net/p/teachingbox/core teachingbox-core Documentation: https://sourceforge.net/p/teachingbox/documentation/HEAD/tree/trunk/manual/
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Tangent
Source-to-source debuggable derivatives in pure Python
Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative... -
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This application allow user to predict dissolution profile of solid dispersion systems based on algorithms like symbolic regression, deep neural networks, random forests or generalized boosted models. Those techniques can be combined to create expert system. Application was created as a part of project K/DSC/004290 subsidy for young researchers from Polish Ministry of Higher Education.
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Seldon Server
Machine learning platform and recommendation engine on Kubernetes
Seldon Server is a machine learning platform and recommendation engine built on Kubernetes. Seldon reduces time-to-value so models can get to work faster. Scale with confidence and minimize risk through interpretable results and transparent model performance. Seldon Core focuses purely on deploying a wide range of ML models on Kubernetes, allowing complex runtime serving graphs to be managed in production. Seldon Core is a progression of the goals of the Seldon-Server project but also a more... -
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NNVM
Open deep learning compiler stack for cpu, gpu
The vision of the Apache NNVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. Compilation of deep learning models into minimum deployable modules. Infrastructure to automatically generates and optimize models on more backend with better performance.... -
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OWL Machine Learning
Machine learning algorithm using OWL
Feature construction and selection are two key factors in the field of Machine Learning (ML). Usually, these are very time-consuming and complex tasks because the features have to be manually crafted. The features are aggregated, combined or split to create features from raw data. This project makes use of ontologies to automatically generate features for the ML algorithms. The features are generated by combining the concepts and relationships that are already in the knowledge base,... -
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Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
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The KReator project is a collection of software systems, tools, algorithms and data structures for logic-based knowledge representation. Currently, it includes the software systems KReator and MECore and the library Log4KR: - KReator is an integrated development environment (IDE) for relational probabilistic knowledge representation languages such as Bayesian Logic Programs (BLPs), Markov Logic Networks (MLNs), Relational Maximum Entropy (RME), First-Order Probabilistic...
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Speakable Programming for Every Language
Your language to speak with all.
This project has the language data for spel, the main new codebase is at: https://gitlab.com/liberit/pyac A computer programming language using human language syntax for human-to-human and human-to-computer communication with high precision, supporting many languages. Currently has alpha prototype support for analytic versions of the UN languages English, Mandarin Chinese, Spanish, Arabic, Russian... -
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JCLTP
A Java Class Library for Text Processing
JCLTP is a class library designed for processing text. JCLTP is free, open source and developed with the Java programming language. JCLTP is distributed under the GNU license. It incorporates several technologies that enable process information while applying AI techniques, in order to build predictive models for text classification. Through a flexible structure of interfaces and classes, the opportunity to extend, adapt and add functionality JCLTP is provided. -
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BagaturChess
Java Chess Engine
This is UCI Chess Engine writen in Java. Since version 1.4 (inclusive) the project was moved to https://github.com/bagaturchess/Bagatur -
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LAML:Linear Algebra and Machine Learning
A stand-alone Java library for linear algebra and machine learning
LAML is a stand-alone pure Java library for linear algebra and machine learning. The goal is to build efficient and easy-to-use linear algebra and machine learning libraries. The reason why linear algebra and machine learning are built together is that full control of the basic data structures for matrices and vectors is required to have fast implementation for machine learning methods. Additionally, LAML provides a lot of commonly used matrix functions in the same signature to MATLAB, thus... -
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Java Library for Machine Learning
A pure Java library for machine learning
JML is a machine learning library in Java, it is a pure Java package, and thus is cross-platform. The goal of JML is to make machine learning methods very easy to use and speed up code conversion from MATLAB to Java. Please be noted that JML has been replaced by LAML. -
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openModeller is a complete C++ framework for species potential distribution modelling. The project also includes a graphical user interface, a web service interface and an API for Python.
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This site contains four packages of Mass and mass-based density estimation. 1. The first package is about the basic mass estimation (including one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation). This packages contains the necessary codes to run on MATLAB. 2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system. 3. The third package DEMassBayes includes the source and object files of a Bayesian...
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FPF_predict
Fine Particle Fraction (FPF) predictor
Application implements models described by classical mathematical equation for in vitro deposition prediction based on characteristics of formulation and assay conditions. This work was funded by Poland-Singapore bilateral cooperation project no 2/3/POL-SIN/2012. Published article: https://www.dovepress.com/empirical-modeling-of-the-fine-particle-fraction-fornbspcarrier-based--peer-reviewed-fulltext-article-IJN -
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HYBRYD
Library written in C with Python API for IPv6 networking
This project is a rewritten of an initial project that I've called GLUE and created in 2005. I'm trying to readapt it for Python 2.7.3 and GCC 4.6.3 The library has to be build as a simple Python extension using >python setup.py install and allows to create different kind of servers, clients or hybryds (clients-servers) over (TCP/UDP) using the Ipv6 Protocol. The architecture of the code is based on brain architecture. Will put an IPv6 adress active available as soon as...
