Machine Learning slings its web: Deeplearn.js is here!

Machine learning has been the talk of the town! With implementations in large number of organizations to carry out prediction and classification tasks. Machine learning is cutting edge in identifying data and processing it to generate meaningful insights based on predictive analytics. But to leverage machine learning, huge computational resources are required. While many may think of it as rocket science, Google has simplified machine learning access to everyone through Deeplearn.js - an initiative that allows ML to run entirely on a web browser.

Deeplearn.js is an open source WebGL- accelerated JS library. This Google PAIR’s initiative (to study and redesign human interactions with ML) aims to make ML available for everyone. This implies that it will not be restricted to specific groups of people such as developers or any businesses implementing it.

Deeplearn.js + browser: A perfect match?

We can say browsers such as Chrome, Internet explorer, Safari, etc are an integral part of our life as it connects us with the world. Their accessibility feature is visible in PWAs’(Progressive Web Apps) wherein applications can run on browsers without the need to download them. In a similar way, machine learning can be carried out within browsers without the fuss of downloading or installing any computational resources. Wonder how? With Deeplearn.js!

Deeplearn.js specifically written in Javascript, is exclusively tailored for machine learning to function on web browsers. It offers an interactive client-side platform which helps them carry out rapid prototyping and visualizations.

Machine learning involves rapid computations with huge CPU requirements and is a complete mismatch for Javascript because of its speed limit. Deeplearn.js is a work-around that allows ML to be implemented using Javascript via the WebGL Javascript API. Additionally, you can use hardware accelerators such as GPUs via the webGL to perform faster and excellent computations with 2D and 3D graphics.

Basic Mechanism - The structure of Deeplearn.js is a blend of Tensorflow and NumPy, which are Python-based packages for scientific computing. The NumPy acts as a quick execution model and the TensorFlow API provides a delayed execution model. Though TensorFlow is a fast and scalable framework widely used by researchers and developers. However, creating web applications on the browser with TensorFlow is difficult as it lacks runtime support to create web applications. Deeplearn.js allows TensorFlow model capabilities to be imported on the browser. By using the tools within Deeplearn.js, weights from the TensorFlow model can be exported.
Opportunities for business - Traditional businesses shy away from using latest ML tools as computational resources are expensive and complicated. Also, due to the complexities in ML, there is a need to hire a technical expert. Through Deeplearn.js, firms can now easily access advanced ML tools and resources. It can not only help them solve data centric business problems but also additionally provide them with innovative strategies, increased competition and improved advantages to stay ahead of their competitors.
Differentiating factor - Deeplearn.js is not the only inbrowser ML library. There are other competing frameworks such as ConvNetJS and Tensorfire, a much recent and almost identical framework to deeplearn.js. A unique feature that differentiates deeplearn.js is its capability to perform faster inference, along with full back propagation.

Implementations with Deeplearn.js

Performance RNN aids in generating music with expressive timing and dynamics. It has been successfully ported into the browser using the Deeplearn.js environment after being trained in TensorFlow. The training data used was the Yamaha e-Piano Competition dataset, which includes MIDI captures of ~1400 performances by skilled pianists.

Teachable Machine is built using Deeplearn.js library. It allows users to teach a machine via a camera with live teaching and without any requirement to code.

Faster Neural Style Transfer algorithm allows in-browser image style transfer. It transfers the style of an image into the content of another image.

To explore other practical projects on Deeplearn.js, you may visit the GitHub repository here.

Deeplearn.js, with the fusion of Machine learning has opened new opportunities and focus areas for businesses and non-developers. SME’s (Subject Matter Expertise) within a business can now grasp deeper insights on how to achieve desired results with Machine learning. The browser is home for many developments which are yet to be revealed in the future. Deeplearn.js truly is a milestone in bringing the web and ML a step closer. However being at the early stage, it would be exciting to see how it unfolds ML for anyone on the planet.