The answer is that a fullstack programmer with knowledge of javascript, Yes, you can do machine learning developments.
Machine learning with Javascript
In general, most of the Machine Learnin appsg are developed using programming languages such as Python; However, we have new tools that make this type of applications more accessible to more developers.
Why should a fullstack web developer know Machine Learning? It may seem like ML is a very different discipline than web development. However, technology demands keeping up with what is happening, not necessarily becoming a super expert on every new technology or tool, but at least having an idea of the possibilities and limits.
And as mentioned before, we now have the ability to explore a new topic without having to also learn another programming language.
And if we analyze how fast things are moving and how powerful the tools are starting to be, in the immediate future a Fullstack programmer with knowledge of Machine Learning will be a highly in-demand job, in fact, it is currently known as ML web developer.
TensorFlow.js.
One of the tools that a Fullstack developer has to program in this field is TensorFlow.js. Is a open source JavaScript library for machine learning. It was developed by Google and is a companion library to TensorFlow, in Python. This tool allows you create machine learning applications that can run in the browser or in Node.js. This way, users do not need to install any software or drivers. Simply opening a web page allows you to interact with a program.
TensorFlow.js works with WebGL and provides a high-level Layers API for defining models and low-level API for linear algebra and supports importing saved Keras models. Another interesting aspect is that you can use either the out-of-the-box JavaScript models, or convert the TensorFlow models to Python and run them in the browser or with Node.js.
And what can we do with Tensorflow.js?
Practically everything that Tensorflow allows us in Python, in fact, the functional structure is very similar:
- Can define tensors and operations, use different TensorFlow.js platforms and environments.
- We have the ability to build a model in TensorFlow.js using the layers and the Core API. Of course it allows us to train models in different training modes. Save, use, and convert models from the TensorFlow.js ecosystem.
- Combine TensorFlow.js and Python's tf.keras.
- Use TensorFlow.js in Node.js.
- Deploy a TensorFlow.js Node project to the cloud.
In short, a Fullstack programmer who is an expert in Javascript can use tools that are out of their field and delve deeper into machine learning, becoming a much more versatile profile desired by the professional market.
