examples
Express
Our great sponsors
examples | Express | |
---|---|---|
143 | 675 | |
7,754 | 63,771 | |
1.2% | 0.7% | |
5.3 | 8.3 | |
27 days ago | 5 days ago | |
Jupyter Notebook | JavaScript | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
examples
-
My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
-
Open Source Ascendant: The Transformation of Software Development in 2024
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.
-
Best AI Tools for Students Learning Development and Engineering
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.
-
Releasing The Force Of Machine Learning: A Noviceโs Guide ๐
TensorFlow: An open-source machine learning framework for high-performance numerical computations, especially well-suited for deep learning.
-
MLOps in practice: building and deploying a machine learning app
The tool used to build the model per se was TensorFlow, a very powerful and end-to-end open source platform for machine learning with a rich ecosystem of tools. And in order to to create the needed script using TensorFlow Jupyter Notebook was used, which is a web-based interactive computing platform.
-
๐ฅ14 Excellent Open-source Projects for Developers๐
10. TensorFlow - Make Machine Learning Work for You ๐ค
-
GPU Survival Toolkit for the AI age: The bare minimum every developer must know
AI models, particularly those built on deep learning frameworks like TensorFlow, exhibit a high degree of parallelism. Neural network training involves numerous matrix operations, and GPUs, with their expansive core count, excel in parallelizing these operations. TensorFlow, along with other popular deep learning frameworks, optimizes to leverage GPU power for accelerating model training and inference.
-
๐ฅ๐ Top 10 Open-Source Must-Have Tools for Crafting Your Own Chatbot ๐ค๐ฌ
#2 TensorFlow
- Are there people out there who still like Sam atlman - AI IS AT DANGER
-
Tensorflow help
I am on a new ftc team trying to get vision to work. I used the ftc machine learning tool chain but I have yet to get a good result with at best a 10% accuracy rate. I have changed everything possible in the tool chain with little luck. To fix this, I have tried making my own .tflite model using the google colab from https://www.tensorflow.org/. When ever I try to run the same code with my own .tflite model, it gives me the error "User code threw an uncaught exception: IllegalStateException - Error getting native address of native library: task_vision_jni". It gives me the same error with official tensor flow tflite test models, and when I put them on a raspberry pi, both worked just fine. Does anyone have a fix to this error or even just tips for the machine learning toolchain?
Express
-
Exploring Angular SSR: Development, API, Prefetching and Deployment
Now, we will create API using expressjs. When we created application using --ssr flag, the Angular CLI already took care of installing expressjs for us.
-
Building a GitHub activity feed with Node.js and Socket.io
First, we import express. The Express framework allows us to create routes that will respond to webhook POST requests and serve an HTML file when a GET request is made to the root of the site.
-
How to Build an AI FAQ System with Strapi, LangChain & OpenAI
Basic Knowledge of Express
-
Building a RESTful API with Node.js and Express
Express.js Documentation
-
7 Frameworks, One SAML Jackson - Your Open Source Single Sign-On Solution
In the JavaScript ecosystem, there are guides for enabling SAML-based enterprise single sign-on in AdonisJS, Express.js, Next.js, Remix, and React with an Express.js backend.
-
8 NPM Packages for JavaScript Beginners [2024][+tutorials]
Starting off strong with Express.js, the cool kid on the block for building web apps. It's lightweight, flexible, and doesn't throw a tantrum when you ask it to scale. With Express, you can handle HTTP requests like a pro, play around with middleware, set up routes without breaking a sweat, and render views that make your app look stunning. Big names like Netflix and Uber are already on board, and if it's good enough for them, it's definitely worth a peek.
-
Full Stack Web Development Concept map
express - one of the most popular middleware tools, lightweight and easy to learn. docs
-
Screen Sharing with WebRTC: Harnessing JavaScript for Seamless Streaming
Now we can install both Express and Socket.io libraries:
-
Express.js: Introduction and Basic Routing
app.listen(3000); ``` Now you can run your server by executing `node index.js`. Your web application will be accessible at http://localhost:3000/, where you'll see "Hello, world!" displayed in your browser. Congratulations! ๐ You've successfully set up basic routing with Express.js! This guide covered only the tip of the iceberg when it comes to utilizing Express.js features. Explore its extensive documentation (https://expressjs.com/) to discover more possibilities. Remember, with Express.js, you have the power to build efficient and scalable web applications. Happy coding!
-
How to convert exist nodejs/expressjs app from javascript to typescript, the painless way
Converting a large Express.js application from JavaScript to TypeScript can be a challenging task. For many applications, this represents a significant portion of their technical debt, as the process may span many days, if not months, and new changes are typically not allowed during the conversion.
What are some alternatives?
cppflow - Run TensorFlow models in C++ without installation and without Bazel
Next.js - The React Framework
mlpack - mlpack: a fast, header-only C++ machine learning library
SvelteKit - web development, streamlined
awesome-teachable-machine - Useful resources for creating projects with Teachable Machine models + curated list of already built Awesome Apps!
Nuxt.js - Nuxt is an intuitive and extendable way to create type-safe, performant and production-grade full-stack web apps and websites with Vue 3. [Moved to: https://github.com/nuxt/nuxt]
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
AdonisJs Application
Selenium WebDriver - A browser automation framework and ecosystem.
Restify - The future of Node.js REST development
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
fastify - Fast and low overhead web framework, for Node.js