Search Results for "classification" - Page 3
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Showing 110 open source projects for "classification"
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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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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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MLBox
MLBox is a powerful Automated Machine Learning python library
...Fast reading and distributed data preprocessing/cleaning/formatting. Highly robust feature selection and leak detection. Accurate hyper-parameter optimization in high-dimensional space. State-of-the-art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,...) Prediction with model interpretation. MLBox has been developed and used by many active community members. Your help is very valuable to make it better for everyone. -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with... -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ... -
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CollectionViewClassifyMenu
CollectionView's two-level menu can collapse the second-level menu
CollectionViewClassifyMenu is an Objective-C iOS demo project that builds categorized menus using UICollectionView. It demonstrates a two-level classification menu where the second-level menu can collapse and expand. The project also includes a sidebar-style menu with multi-select behavior for filtering choices. It supports persistence of selected filter conditions, so the app can remember the user’s previous menu state after reopening. The demo uses NSCoding for saving filter data and includes a way to clear saved settings at launch. ... -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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CTS Surveyor
Foot traffic and facial analytics for your business and home
Surveyor is a software solution that monitors its environment via camera and gathers demographic information about the public in the surrounding area, providing important statistics such as number of people passing by as well as providing facial analytics to classify the pedestrians based on their age and gender. The statistical data is stored in a local database and is made available via RESTful API’s, and easy integration with other applications can be accomplished via a WebSocket... -
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Omniglot
Omniglot data set for one-shot learning
...It includes both MATLAB and Python starter scripts (e.g. demo.m, demo.py) to illustrate how to load the images and stroke sequences and run baseline experiments (such as classification by modified Hausdorff distance). The dataset provides both an image representation of each character and the time-ordered stroke coordinates ([x, y, t]) for each instance. Includes stroke data (time-sequenced coordinates) per sample. The repository is intended as a benchmark dataset in few-shot / meta-learning research, not as a plug-and-play detection or classification engine. ... -
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
video-nonlocal-net implements Non-local Neural Networks for video understanding, adding long-range dependency modeling to 2D/3D ConvNet backbones. Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo... -
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Simd
High performance image processing library in C++
...It provides many useful high performance algorithms for image processing such as: pixel format conversion, image scaling and filtration, extraction of statistic information from images, motion detection, object detection (HAAR and LBP classifier cascades) and classification, neural network. The algorithms are optimized with using of different SIMD CPU extensions. In particular the library supports following CPU extensions: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2 and AVX-512 for x86/x64, VMX(Altivec) and VSX(Power7) for PowerPC, NEON for ARM. The Simd Library has C API and also contains useful C++ classes and functions to facilitate access to C API. ... -
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cnn-text-classification-tf
Convolutional Neural Network for Text Classification in Tensorflow
The cnn-text-classification-tf repository by Denny Britz is a well-known educational implementation of convolutional neural networks for text classification using TensorFlow, aimed at helping developers and researchers understand how CNNs can be applied to natural language processing tasks. Based loosely on Kim’s influential paper on CNNs for sentence classification, this codebase demonstrates how to preprocess text data, convert words into learned embeddings, and apply multiple convolution filters to extract n-gram features that are then pooled and fed into a classifier. ... -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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Mixup-CIFAR10
mixup: Beyond Empirical Risk Minimization
mixup-cifar10 is the official PyTorch implementation of “mixup: Beyond Empirical Risk Minimization” (Zhang et al., ICLR 2018), a foundational paper introducing mixup, a simple yet powerful data augmentation technique for training deep neural networks. The core idea of mixup is to generate synthetic training examples by taking convex combinations of pairs of input samples and their labels. By interpolating both data and labels, the model learns smoother decision boundaries and becomes more... -
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...When you have more than 1,000 websites or articles in your browser's favorites, finding things is definitely a labor of life. Then let the cloud collection help you solve it, which is convenient for classification, organization, query and search. Cloud Collection is an open source website built with Spring Boot, which allows users to collect a website online anytime, anywhere, and categorize the collected websites or articles on the website, which can be used as temporary storage for later reading.
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FS-Blog
Personal blog, the pioneering work of Spring Boot,
Personal blog, the pioneering work of Spring Boot, using Spring Boot + MyBatis, front-end Bootstrap + LayUI, supports the lightweight Markdown editor Editor.md favored by programmers, and supports tag classification retrieval. Core framework is SpringBoot, ORM framework is MyBatis, MyBatis toolis MyBatis Mapper, MVC frameworks is Spring MVC, Template engine is Freemarker, Compilation auxiliary plug-in: Lombok, CSS Framework is BootStrap 4.0, Markdown editor is Editor.md, and Database is MySQL -
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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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Deeplearning-papernotes
Summaries and notes on Deep Learning research papers
Deeplearning-papernotes is an implementation of Convolutional Neural Networks for sentence and text classification in TensorFlow, based on a well-known research paper that applies CNN architectures to natural language processing tasks with strong performance in sentiment analysis and similar classification problems. The repository provides the complete network definition, including an embedding layer to convert words into dense representations, convolution and max-pooling layers to extract informative features, and a final softmax classifier to distinguish between target classes. ... -
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Keras resources
Directory of tutorials and open-source code repositories
...It aggregates a wide range of resources, including beginner guides, advanced tutorials, code examples, and third-party tools, all organized into a single reference hub. The repository covers diverse topics such as image classification, natural language processing, reinforcement learning, and generative models, providing both theoretical and practical insights. It also includes links to external projects built with Keras, demonstrating real-world applications of deep learning techniques. The structure is designed for easy navigation, allowing users to quickly find relevant materials based on their skill level or area of interest. ... -
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Accord.NET Framework
Machine learning, computer vision, statistics and computing for .NET
The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and extensive documentation and a wiki help fill in the details. The Accord.NET project provides... -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
...The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). It allows researchers and practitioners to apply node2vec to various graph datasets and evaluate embedding quality on downstream tasks. By bridging ideas from graph theory and word embedding models, this project demonstrates how graph-based machine learning can be made efficient and flexible. -
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Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. -
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JCLTP
A Java Class Library for Text Processing
...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. Thus, analysis of new types of information is much easier and intuitive. The researcher can use the class library by adding it to his project or direct through specific commands created for these cases. ... -
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Genetic Programming Classifier is a distributed evolutionary data classification program. It uses the ensemble method implemented under a parallel co-evolutionary Genetic Programming technique.
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GTkNN
GPU-based Textual kNN (GT-kNN)
The following code is a parallel kNN implementation that uses GPUs for the high dimensional data in text classification. You can use it to classify documents using kNN or to generate meta-features based on the distances between a query document and its k nearest neigbors -
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The JINSECT toolkit is a Java-based toolkit and library that supports and demonstrates the use of n-gram graphs within Natural Language Processing applications, ranging from summarization and summary evaluation to text classification and indexing.
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This application assists in generating pre-compositional material based on a computational interpretation of the 'Schillinger System of Musical Composition' (Schillinger 1946).
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...The FOSSology Project is a Free Open Source Software (FOSS) project built around an open and modular architecture for analyzing software for open source software governance. Existing modules include license scanning, copyright and user identification, license classification and meta data extraction.
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libVMR
VMR - machine learning library
...libVMR is based on the most popular neural network model with a higher generalization ability from kernel tricks - vector machine by Reshetov (VMR). The library has been designed to learn from data sets. Typical applications here are pattern recognition ( binary classification).
