TensorFlow.js is a library for developing and training machine learning models in JavaScript and deploying them in a browser or on Node.js.

It is an open source, hardware-accelerated JavaScript library for training and deploying machine learning models.

Amazing to see the innovation of TensorFlow combined with the reach of more developers.

In recent times, a lot of attention is being paid to artificial intelligence (AI) and machine learning (ML). And in this context, the two most popular technologies are the Python and R environments or even C++ libraries. One of the most popular frameworks among developers is TensorFlow, which was developed by Google in 2011. Most of TensorFlow was designed in C++ and has bindings to Python or Java or R, but the most crucial language is missing, which is JavaScript.

Siraj Raval shows off examples of machine learning apps from his students.

If you’re wondering about my stance on the recent controversies around Siraj, I recorded a Data Point about that.

Machine Learning powers almost every internet service we use these days, but it’s rare to find a full pipeline example of machine learning being deployed in a web app. In this episode, I’d like to present 5 full-stack machine learning demos submitted as midterm projects from the students of my current course. The midterm assignment was to create a paid machine learning web app, and after receiving countless incredible submissions, I’ve decided to share my favorite 5 publicly. I was surprised by how many students in the course had never coded before and to see them building a full-stack web app in a few weeks was a very fulfilling experience. Use these examples as a template to help you ideate on potential business ideas to make a positive impact in the world using machine learning. And if you’d like, be sure to reach out and support each of the students I’ve demoed here today in any way can you offer. They’ve been working their butts off. Enjoy!

Presentation notebook: https://colab.research.google.com/drive/1m5aLHPnwIhVX8zgMvZUtK4iG9xSaMbk8