NanoNets is a Deep Learning web platform that makes it easier than ever before to use Deep Learning in practical applications. It combines the convenience of a web-based platform with Deep Learning models to create image recognition and object classification applications for your business. You can easily build and integrate deep learning models using NanoNets’ API. You can also work with our pre-trained models which have been trained on huge datasets and return accurate results. NanoNets has leveraged recent advances in Deep Learning to build rich representations of data which are transferable across tasks. It’s as simple as uploading your input, generating the output and getting a functioning and highly accurate Deep Learning model for your AI needs. NanoNets is revolutionary because it allows you to train models without large datasets. With just 100 images you can train a model on our platform to detect features and classify images with a high degree of accuracy. NanoNets benefits you in four important ways: ● It reduces the amount of data needed to build a Deep Learning Model ● NanoNets handles the infrastructure for hosting and training the model, and for the run time ● It reduces the cost of running deep learning models by sharing infrastructure across models ● It is possible for anyone to build a deep learning model
Based on our record, Nanonets should be more popular than SignalR. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Blazor Server basically has the server remote control puppet everything on the client through SignalR. Source: 11 months ago
SignalR is a layer over websockets, and is available for python. Source: about 1 year ago
Since Go is a pretty simple game and not very graphic intensive, a simple approach would be to use SignalR on ASP.NET, where the server maintains the game board state and just sends minimal messages (for example, piece X moved to location Y, and whose turn it is now) to each player after their respective move in turn. Source: about 1 year ago
SignalR and Pinia for real-time stat updates in the dashboard UI. Source: about 1 year ago
Want to automate repetitive manual tasks? Check our Nanonets workflow-based document processing software. Source: almost 2 years ago
Nanonets is a no-code, workflow-based, and AI-enhanced intelligent document processing platform. It automates all document processes and is built on a robust, intelligent, self-learning OCR API that allows users to extract required data from documents in minutes. Source: almost 2 years ago
Check out our website here https://nanonets.com/ for more. We also have some free tools where you can experience our product for free (like https://nanonets.com/online-ocr). Source: almost 2 years ago
Here is another company, which I just came across by accident, which do the same: https://nanonets.com/. Source: about 2 years ago
We will be using Python3.6+, Django web framework, Nanonets for character extraction from an image, Cloudinary for image storage and Google Search API for performing the searches. - Source: dev.to / over 2 years ago
Socket.io - Realtime application framework (Node.JS server)
Docsumo - Extract Data from Unstructured Documents - Easily. Efficiently. Accurately.
Firebase - Firebase is a cloud service designed to power real-time, collaborative applications for mobile and web.
DocParser - Extract data from PDF files & automate your workflow with our reliable document parsing software. Convert PDF files to Excel, JSON or update apps with webhooks.
Pusher - Pusher is a hosted API for quickly, easily and securely adding scalable realtime functionality via WebSockets to web and mobile apps.
Amazon Textract - Easily extract text and data from virtually any document using Amazon Textract. Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables.