In the past few years, Python has become the preferred programming language for machine learning and deep learning.
But Python is not the only option for programming machine learning applications.
Theres a growing community of developers who are using JavaScript to run machine learning models.
#1.Private machine learning
Mostmachine learningapplicationsrely on client-server architectures.
Users must send their data where the machine learning models are running.
There are clear benefits to the client-server architecture.
Developers can run their models on servers and make them available to user applications through web APIs.
This makes it possible for developers to use very largeneural networksthat cant run on user devices.
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Fortunately, not all machine learning applications require expensive servers.
Many models can be compressed to run on user devices.
And mobile rig manufacturers are equipping their devices with chips to support localdeep learninginference.
But the problem is that Python machine learning is not supported by default on many user devices.
Windows users must install Python manually.
And mobile operating systems have very poor support for Python interpreters.
JavaScript, on the other hand, is natively supported by all modern mobile and desktop browsers.
This means JavaScript machine learning applications are guaranteed to run on most desktop and mobile devices.
There are already several JavaScript machine learning libraries.
An example is TensorFlow.js, the JavaScript version of Googles famous TensorFlow machine learning and deep learning library.
They will launch the machine learning models on your equipment without sending any data to the cloud.
And you dont need to install any additional software.
Other powerful JavaScript machine learning libraries includeML5.js,Synaptic, andBrain.js.
Examples of TensorFlow.js applications
#2.
Fast and customized ML models
Privacy is not the only benefit of on-gadget machine learning.
Another important use for JavaScript machine learning is model customization.
And users would still be able to use their machine learning capabilities even when theyre disconnected from your servers.
Client-side machine learning allows developers to run customized models on user devices
#3.
Python support in mobile operating systems is still in the preliminary stages.
But there is already a rich set of cross-platform JavaScript mobile app development tools such as Cordova and Ionic.
These online window objects support JavaScript machine learning libraries.
There are other machine learning libraries for mobile applications, such asTensorFlow LiteandCore ML.
However, they require native coding in the mobile platform you are developing your app for.
JavaScript machine learning, on the other hand, is very versatile.
#4.
JavaScript machine learning on server
One of the main challenges of machine learning is training the models.
This is especially true for deep learning, where learning requires expensive backpropagation computations over several epochs.
Python is better suited for server-side training of machine learning models.
It can scale and distribute its load on server clusters to accelerate the training process.
Once the model is trained, you might compress it and deliver it on user devices for inference.
Fortunately, machine learning libraries written in different languages are highly compatible.
But it is worth noting that server-side JavaScript machine learning is also maturing.
TensorFlow.js has a special version that is suited for servers running Node.js.
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