Search Results for "java open source" - Page 27
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Showing 930 open source projects for "java open source"
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Context for your AI agentsBuild data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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Edward
A probabilistic programming language in TensorFlow
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It... -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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fscaret_shiny
UI for fscaret
User Interface (ui) application which implements the automated feature selection provided by the 'fscaret' package of R-environment. -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
Say goodbye to broken revenue funnels and poor customer experiencesLeanData is a Demand Management solution that supports all go-to-market strategies such as account-based sales development, geo-based territories, and more. LeanData features a visual, intuitive workflow native to Salesforce that enables users to view their entire lead flow in one interface. LeanData allows users to access the drag-and-drop feature to route their leads. LeanData also features an algorithms match that uses multiple fields in Salesforce.
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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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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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Deepo
Set up deep learning environment in a single command line
Deepo is a series of Docker images that allows you to quickly set up your deep learning research environment, supports almost all commonly used deep learning frameworks, supports GPU acceleration (CUDA and cuDNN included), also works in CPU-only mode, and works on Linux (CPU version/GPU version), Windows (CPU version) and OS X (CPU version). Their Dockerfile generator that allows you to customize your own environment with Lego-like modules, and automatically resolves the dependencies for... -
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Five video classification methods
Code that accompanies my blog post outlining five video classification
Classifying video presents unique challenges for machine learning models. As I’ve covered in my previous posts, video has the added (and interesting) property of temporal features in addition to the spatial features present in 2D images. While this additional information provides us more to work with, it also requires different network architectures and, often, adds larger memory and computational demands.We won’t use any optical flow images. This reduces model complexity, training time, and... -
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Seldon Server
Machine learning platform and recommendation engine on Kubernetes
...Seldon Core is a progression of the goals of the Seldon-Server project but also a more restricted focus to solving the final step in a machine learning project which is serving models in production. Seldon Server is a machine learning platform that helps your data science team deploy models into production. It provides an open-source data science stack that runs within a Kubernetes Cluster. You can use Seldon to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g. GCP, AWS, Azure). -
Lightspeed golf course management softwareFrom tee sheet management, point of sale and payment processing to marketing, automation, reporting and more—Lightspeed is built for the pro shop, restaurant, back office, beverage cart and beyond.
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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. Compilation and minimal runtimes commonly unlock ML workloads on existing hardware. ... -
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UnrealCV
Connecting Computer Vision to Unreal Engine
UnrealCV is a project to help computer vision researchers build virtual worlds using Unreal Engine (UE). It extends UE with a plugin. UnrealCV can be used in two ways. The first one is using a compiled game binary with UnrealCV embedded. This is as simple as running a game, no knowledge of Unreal Engine is required. The second is installing the UnrealCV plugin into Unreal Engine and using the editor to build a new virtual world. -
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. ... -
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auto_ml
Automated machine learning for analytics & production
auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. Before you go any further, try running the code. Load up some data (either a DataFrame, or a list of dictionaries, where each dictionary is a row of data). Make a column_descriptions dictionary that tells us which attribute name in each row represents the value we’re... -
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Genetic Oversampling Weka Plugin
A Weka Plugin that uses a Genetic Algorithm for Data Oversampling
Weka genetic algorithm filter plugin to generate synthetic instances. This Weka Plugin implementation uses a Genetic Algorithm to create new synthetic instances to solve the imbalanced dataset problem. See my master thesis available for download, for further details. -
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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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Deep Learning with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow. To date tensorflow comes in two different packages, namely tensorflow and tensorflow-gpu, whether you want to install the framework with CPU-only or GPU support, respectively. NVIDIA Drivers and CuDNN must be installed and configured before hand. Please refer to the official Tensorflow documentation for further details. Since version 0.9 Theano introduced the libgpuarray in the stable release (it was previously only available in... -
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This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
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Chronological Cohesive Units
The experimental source code for the paper
The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming" -
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Lip Reading
Cross Audio-Visual Recognition using 3D Architectures
The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Coupled 3D Convolutional Neural Networks for audio-visual matching. Lip-reading can be a specific application for this work. Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the... -
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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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Machine Learning for OpenCV
M. Beyeler (2017). Machine Learning for OpenCV
M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4. -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform... -
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DeepLearningProject
An in-depth machine learning tutorial
This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made. The dataset is not one of the standard sets like MNIST or CIFAR, you... -
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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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GPU Machine Learning Library. This library aims to provide machine learning researchers and practitioners with a high performance library by taking advantage of the GPU enormous computational power. The library is developed in C++ and CUDA.
