Backbone.React.Component is a mixin and API that glues Backbone models and collections into React components. When used as a mixin the component is mounted, a wrapper starts listening to models and collections changes to automatically set your component state and achieve UI binding through reactive updates.
backbone react data-binding models collections server client react-componentWe've put up the largest collection of machine learning models in Core ML format, to help iOS, macOS, tvOS, and watchOS developers experiment with machine learning techniques. We've created a site with better visualization of the models CoreML.Store, and are working on more advance features. If you've converted a Core ML model, feel free to submit an issue.
coreml coreml-model apple machine-learning curated-list coreml-framework coreml-models coremltools awesome-list models model download awesome core-ml ml caffe caffemodel tensorflow-models ios ios11Microstates makes working with pure functions over immutable data feel like working with the classic, mutable models we all know and love. But, most imporantly, Microstates makes working with state fun.
composition state-machines lens microstates transition batch-transitions state-machine immutable composable models stateBolt is an experimental security and rules compiler for Firebase Realtime Database (not for Firebase Cloud Storage). It is currently in beta. The language definition is converging, but not yet finalized. We welcome experimentation, but ask that you hand-verify the resulting JSON output before using with production applications. Otherwise, we'd love to have feedback from early adopters. You can email questions to firebase-talk@googlegroups.com using "Bolt" in the subject line, or post bugs on our Issue Tracker.
firebase security rules schema models blazeThis repository contains scripts and documentation to use TensorFlow image classification and object detection models on NVIDIA Jetson. The models are sourced from the TensorFlow models repository and optimized using TensorRT. Flash your Jetson TX2 with JetPack 3.2 (including TensorRT).
neural-network tensorflow models realtime inference optimize nvidia image-classification object-detection train tx1 jetson tensorrt tx2A pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self learning car. You can spend years to build a decent image recognition algorithm from scratch or you can take inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures.
data-science image computer-vision deep-learning neural-network mxnet tensorflow model models keras python3 pytorch model-selection image-classification awesome-list object-detection pretrained-models pretrained video-analysisQuickly create customizable UI components around your models. Gradio makes it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images, pasting your own text, recording your own voice, etc. and seeing what the model outputs. You can find an interactive version of this README at https://gradio.app/getting_started.
data-science machine-learning ui deep-learning interface deploy models data-visualization data-analysis ui-components gradio python-notebook gradio-interfaceFlexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions. It supports aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets, High performance merging and joining of data sets, Time series-functionality, Hierarchical axis indexing and lot more.
data-analysis data econometrics models numpy statistics tables tabular timeseriesI created this package in response to a client project that had complex, nested forms with many <select>'s that resulted in over 700 database queries on one page. I needed a package that abstracted the caching process out of the model for me, and one that would let me cache custom queries, as well as cache model relationships. This package is an attempt to address those requirements. For best performance a taggable cache provider is recommended (redis, memcached). While this is optional, using a non-taggable cache provider will mean that the entire cache is cleared each time a model is created, saved, updated, or deleted.
laravel laravel-5-package caching modelsCoolie parse a JSON file to generate models (& their constructors). Coolie has a Baby now.
json json-parser model models struct class generatorPlank is a command-line tool for generating robust immutable Objective-C models from JSON Schemas. It will save you time writing boilerplate and eliminate model errors as your application scales in complexity. Models are defined in JSON, a well-defined, extensible and language-independent specification.
models ios flowtype android schema adt jsonValidators for Mongoose schemas utilising validator.js. Mongoose Validator simply returns Mongoose style validation objects that utilises validator.js for the data validation.
mongoose validate validation models schemaMongoose plugin to enable population of nested models at any level of depth. Support Mongoose 3.8.x and later. Refer to changelog for breaking changes. Specify whitelist option to ensure only certain paths can be populated. This is to prevent potential performance and security issues if you allow API clients to supply population paths.
mongoose mongoose-plugin population mongo mongodb populate deep-populate deep-population models nested-models populate-models model-population documents nested-documents populate-documents documents-populationIf you don't know how to define types with tcomb you may want to take a look at its README file. Note.
tcomb validation models domainTs.ED is a framework on top of Express to write your application with TypeScript (or in ES6). It provides a lot of decorators to write your code. Important! TsExpressDecorators requires Node >= 6, Express >= 4, TypeScript >= 2.0 and the experimentalDecorators, emitDecoratorMetadata, types and lib compilation options in your tsconfig.json file.
typescript decorators express-decorators express-router nodejs lifecycle-hooks middleware converter ioc dependency-injection rest-api swagger-ui socket-io multer express router controllers services middlewares models mvc injection di api swagger mongoose ajv socketio es2015 es6 server rest validationYou will need to install jekyll to render and edit the documentation.
vue vuejs data-structures models collections validation collection datastore model store structuresSilverlight 3D Model Importer allows to work with models processed using Xna Content Pipeline inside a Silverlight 5 (beta) project. It includes all vertex / index buffers data, as well, as textures, bones and mesh / mesh parts info, and provide easy methods to render in SL
3d models silverlight-5 xnaThe ONNX Model Zoo is a collection of pre-trained models for state-of-the-art models in deep learning, available in the ONNX format. Accompanying each model are Jupyter notebooks for model training and running inference with the trained model. The notebooks are written in Python and include links to the training dataset as well as references to the original paper that describes the model architecture. The notebooks can be exported and run as python(.py) files. The Open Neural Network eXchange (ONNX) is a open format to represent deep learning models. With ONNX, developers can move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.
onnx models download pretrained deep-learning
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