Search Results for "model-builder" - Page 10
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Showing 370 open source projects for "model-builder"
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
...You'll have to have an Nvidia GPU and you'll have to install CUDA. The CMakeLists.txt file automatically detects if you have CUDA installed or not. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. -
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TableView for Android
Powerful Android library for displaying complex data structures
TableView is a powerful Android library for displaying complex data structures and rendering tabular data composed of rows, columns and cells. TableView relies on a separate model object to hold and represent the data it displays. This repository also contains a sample app that is designed to show you how to create your own TableView in your application. -
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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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NLP Architect
A model library for exploring state-of-the-art deep learning
...NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a model-oriented library designed to showcase novel and different neural network optimizations. The library contains NLP/NLU-related models per task, different neural network topologies (which are used in models), procedures for simplifying workflows in the library, pre-defined data processors and dataset loaders and misc utilities. The library is designed to be a tool for model development: data pre-processing, build model, train, validate, infer, save or load a model. -
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Easy Rules
The simple, stupid rules engine for Java
...Please consider upgrading to this version at your earliest convenience. Lightweight library and easy-to-learn API. POJO-based development with an annotation programming model. Useful abstractions to define business rules and apply them easily with Java. The ability to create composite rules from primitive ones. The ability to define rules using an Expression Language (Like MVEL, SpEL and JEXL) -
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CSSBox
Pure Java HTML / CSS rendering engine
CSSBox is an (X)HTML/CSS rendering engine written in pure Java. Its primary purpose is to provide a complete information about the rendered page suitable for further processing. However, it also allows displaying the rendered document. -
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QtExtensions
Graphical and non graphical extensions to the Qt framework.
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models... -
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Jodd
Jodd! Lightweight, Java, zero dependencies, use what you like
Jodd is a set of micro-frameworks and developer-friendly tools and utilities. Code-things-done. Great performances. Tiny dependencies. Use what you like. Welcome to the Jodd, a unique, alternative world of Java. You can find here some micro-frameworks and some handy tools. Nothing more, nothing less. The decision which component is promoted is based on the Maven Central Repo statistics. Version v5 contains remaining libraries and frameworks. They are in maintenance mode. -
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SentEval
A python tool for evaluating the quality of sentence embeddings
...Datasets are wrapped with unified preprocessing and metrics so results are comparable across papers and implementations. Because the interface is minimal, researchers can plug in encoders from any framework or language model and obtain a broad evaluation with little glue code. SentEval helped establish common baselines and reporting conventions in the sentence-representation community, reducing friction when comparing new methods. -
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Sparse Attention
"Generating Long Sequences with Sparse Transformers" examples
Sparse Attention is OpenAI’s code release for the Sparse Transformer model, introduced in the paper Generating Long Sequences with Sparse Transformers. It explores how modifying the self-attention mechanism with sparse patterns can reduce the quadratic scaling of standard transformers, making it possible to model much longer sequences efficiently. The repository provides implementations of sparse attention layers, training code, and evaluation scripts for benchmark datasets. ... -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. 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... -
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Higher
higher is a pytorch library
...It allows developers and researchers to compute gradients through entire optimization processes, which is essential for tasks like meta-learning, hyperparameter optimization, and model adaptation. The library introduces utilities that convert standard torch.nn.Module instances into “stateless” functional forms, so parameter updates can be treated as differentiable operations. It also provides differentiable implementations of common optimizers like SGD and Adam, making it possible to backpropagate through an arbitrary number of inner-loop optimization steps. ... -
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The Neural Process Family
This repository contains notebook implementations
...Each notebook includes theoretical explanations, key building blocks, and executable code that runs directly in Google Colab, requiring no local setup. Implementations rely only on standard dependencies such as NumPy, TensorFlow, and Matplotlib, and provide visualizations of model performance. -
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libagents
Multi-threaded C++11 Actor Model library
libagents is a C++11 implementation of the "Actor Model" (http://en.wikipedia.org/wiki/Actor_model). The libagents API allows for a natural decoupling of the core application logic from the application shell, thus facilitating the development of easily portable applications. Tested with: * gcc-4.9.1_x86 (Qt-5.4.2_windows_x86) on WinXP_x86, Win7_x86, Win8.1_x64 * gcc-7.3.0_x64 (Qt-5.9.5_linux_x64) on Ubuntu18.04_x64 -
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Util
Java util library with many goodies that will blow Your mind.
My Java util library. It contains many variations of Enumerations, Iterators, Collections, Sets and Maps (e.g. Map2D, Map3D, InvertibleMap, SetMap). It also contains very nice Model-View-Adapter framework. Necessarily see the AnotherClass interface. It will blow your mind. Furthermore it contains interfaces like: Hierarchical, Factory, Converter, Overridable, Persistable, Bindable, Sequential and many other. -
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RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
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PyTorch Natural Language Processing
Basic Utilities for PyTorch Natural Language Processing (NLP)
...PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. Now you've setup your pipeline, you may want to ensure that some functions run deterministically. Wrap any code that's random, with fork_rng and you'll be good to go. Now that you've computed your vocabulary, you may want to make use of pre-trained word vectors to set your embeddings. -
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Incremental DOM
An in-place DOM diffing library
Incremental DOM is a lightweight library for building DOM trees by issuing imperative update instructions, avoiding heavyweight virtual DOM diffing at runtime. Instead of creating and diffing large object graphs, templates compile to a sequence of function calls that “patch” the live DOM in place. This model eliminates allocations associated with virtual trees and allows updates to be streamed directly to the DOM, which can improve memory usage and reduce GC pressure. It integrates naturally with template compilers (such as those that generate calls from HTML-like markup) but can also be used by hand for fine-tuned rendering. The library is small and focused: it manages element creation, attribute updates, and text nodes while leaving state management and data flow to the host framework. ... -
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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 both RGB and optical flow input streams. The models have achieved state-of-the-art results on benchmark datasets such as UCF101 and HMDB51, and also won first place in the CVPR 2017 Charades Challenge. ... -
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html5-canvas-2d-grid-layout
A simple grid model system for drawing shapes in an HTML canvas
This project allows the user to define a grid configuration model for a canvas within an HTML document. The grid configuration allows the user to specify height and width of grid "units". Once the grid configuration is defined, the user can create an draw certain shapes and lines, specifying locations in "units" of where objects should be placed in the canvas. Shapes and lines support configuration themselves for color, stroke width, etc. -
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React Sortable HOC
Turn any list into an animated, accessible and sortable list
...It provides a set of utilities that allow developers to create sortable interfaces with smooth animations and minimal effort. The library is built on top of React’s component model, making it easy to integrate into existing applications. It supports features such as keyboard accessibility, customizable drag handles, and dynamic list updates. The system is designed to handle both simple and complex sorting scenarios, including nested lists and grid layouts. It also includes performance optimizations to ensure smooth interactions even with large datasets. ... -
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RobotsDisallowed
A curated list of the most common and most interesting robots.txt
RobotsDisallowed is a public catalog that tracks websites and organizations explicitly blocking AI and web-scraping crawlers in their robots.txt or related mechanisms. It focuses on documenting the growing trend of content owners asserting control over how their data is used for model training and automated harvesting. The project aggregates domains, notes the targeted bots or user agents, and surfaces patterns for researchers, policymakers, and tool builders. It serves both as a transparency effort and as a resource for people designing allow/deny strategies for automated access. The dataset invites community contributions to keep the picture current as new bots emerge and policies shift. ... -
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pytorch-examples
Simple examples to introduce PyTorch
...The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. By emphasizing readable code, the repository helps users understand how PyTorch’s imperative programming style enables flexible model development. It also serves as a quick reference for common patterns and techniques used in deep learning workflows. The project aligns with PyTorch’s philosophy of combining usability with performance and flexibility. Overall, pytorch-examples is an essential learning resource for anyone working with PyTorch. -
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TenorSpace.js
Neural network 3D visualization framework
...TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports the visualization of pre-trained models from TensorFlow, Keras and TensorFlow.js. TensorSpace is a neural network 3D visualization framework designed for not only showing the basic model structure but also presenting the processes of internal feature abstractions, intermediate data manipulations and final inference generations. ...
