One of the things that makes TensorFlow such a popular deep learning library is its flexibility.
Compatible to some degree with numerous programming languages, it can be used to aid in deep and traditional machine learning across multiple sectors — Thanks Google Brain team!
As great as this is, however, it does pose something of a conundrum… With so many programming languages to choose from, which is best for your AI project?
Well, by and large, your two main options are TensorFlow Py and TensorFlow Js.
That said, those two-letter suffixes don’t really give much away, so you’re probably still left wondering what route you should take, which is why, in today’s post, I’ll be breaking down the primary differences between these TensorFlow-compatible programming languages.
Before We Begin
Let’s get the basics out of the way first.
TensorFlow is a deep learning library, whereas Js and Py are programming languages that can be used to implement TensorFlow modules and functions, so we cannot compare TensorFlow itself to Js or Py.
Instead, we’ll compare TensorFlow Js with TensorFlow Py.
What Is Py?
Py stands for Python, which is an object-oriented, high-level programming language, meaning that it cannot be understood by computers as is. It must run through a translator of sorts before a machine can execute commands based on the data given in the script.
Built-in data structures as well as dynamic typing and binding make it an incredibly useful language for rapid application development.
These features also make it a powerful glue language, which is to say that it can be used to seam multiple pre-existing components together.
What Are The Benefits Of Python?
Python brings a myriad of benefits to the table for programmers.
Firstly, it’s an incredibly efficient language, and thus, it tends to increase productivity significantly. Python has a debugger written into the language itself, meaning weeding out issues is a total breeze.
There are also a few other Python benefits to mention, including its syntactic simplicity that positions it as a fantastic choice for newcomers to coding who want to ease themselves into a language that isn’t too impenetrable and hard to remember.
Rich 3rd-Party Affiliations
Python is linked to a fleshed-out array of third-party frameworks, libraries, and packages that can streamline development, particularly where large-scale projects are concerned.
Despite the focus on readability, you’ll also enjoy a certain amount of flexibility with Python.
Allowing developers to choose either procedural or object-based programming modes, it’s suitable for a wealth of different projects, and as it uses 5 data types (string, number, tuple, dictionary, and list), data analysis is remarkably easy to conduct.
Python is supported by all modern operating systems, meaning teams won’t have to use the same hardware to work collaboratively.
Python has one of the strongest communities of all programming languages. Thousands upon thousands of users contribute to the Python toolbox and provide support for one another.
What Is Js?
In fact, it’s something of a celebrity in the world of programming languages, as it’s one of the primary tools used to form the internet as we know it.
It’s also a very simple programming language, making it an enticing prospect for beginners to programming, as well as professional developers hoping to increase productivity.
Enhanced Website & Application Performance
Python & TensorFlow
Although TensorFlow is compatible with a number of different programming languages, many consider Python to be its native language, as well as the best one to use due to its unparalleled simplicity and robust community and support network.
You’ll also get the benefit of Tensorboard when using Python, a facility that provides the tooling and visualization necessary for experimentation in machine learning, something that Js doesn’t support.
TensorFlow Py is incredibly stable too, meaning it’s unlikely to change over time and across different platforms.
The extensive data science libraries it brings to the table are also one of the reasons someone might choose to use Python over other programming languages for implementing TensorFlow.
But, does this mean Python is the best choice for you and your project? Not necessarily.
What does this mean, exactly? Well, Python only works well server-side, which is to say it can only reliably be used to execute code on the web server.
That said, we can assume Python is best for Server-exclusive projects, right? Actually… nope.
TensorFlow Py Vs. TensorFlow Js: Which Should You Use?
Okay, so we’ve covered a lot of ground here today, but the key takeaways are relatively few.
Ultimately, which programming language you choose for your TensorFlow project comes down to the following:
- GPU — You cannot use Python without a discrete GPU, so if you’re relying on integrated graphics facilities, you’ll have to use TensorFlow Js.
The differences outlined above mean that neither TensorFlow Py nor TensorFlow Js is technically superior, rather, they each have their specialties, and which you choose comes down to the nature of your project and hardware.