AI and machine learning is an area of tech that is developing rapidly. If you want to get involved, you should be aware and consider using TensorFlow, a tool for developing ML applications. Pohan Lin – Senior Web Marketing and Localizations Manager at Databricks, tells you more about TensorFlow.
In the world of technology, machine learning is one of the most exciting tools we have available. The ability for machines to “predict†our actions has proven popular, as the rise of digital assistants, like Siri and Alexa, has proven.
However, machine learning and AI are broad concepts, and TensorFlow allows you to explore them a little more easily. That is why we have produced this quick overview of TensorFlow for beginners, to detail its components and requirements. It should help you decide if TensorFlow is useful for your own development projects.
What Is TensorFlow?
To understand TensorFlow properly, we must understand some of the technology surrounding it.
TensorFlow is an open-source, Google-developed tool that allows you to build machine learning apps. It bundles together a few different tools you need to accomplish this. These tools include machine learning models, deep learning models, and relevant algorithms.Â
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It has been boosted by the rise of artificial intelligence and deep learning, which the rise of MLOps platformsOpens a new window reflects the popularity of. TensorFlow itself is used for tasks like voice, speech, and image recognition.
Google also uses these kinds of technologies to enhance things like web search and translation. This probably explains why Google developed it and made TensorFlow open-source. In time, AI and related technologies may help other tools like IVR work more effectively.
To clarify, machine learning and deep learning are interconnected. The latter is a variation of the former, which is, in turn, a branch of artificial intelligence. As its name suggests, machine learning lets machines “learn†from prior actions and make more accurate predictions. Deep learning works similarly but allows machines to handle more complex forms of data.Â
Since AI technology is only growing in popularity (as the rise of chatbotsOpens a new window demonstrates), any tool that helps us to access it is valuable. TensorFlow is one such tool, although it does require a level of pre-existing skill to use properly.Â
See More: Top Five AI Frameworks to Consider in 2022
Why Should I Use TensorFlow?
As a piece of open-source software, TensorFlow is entirely free to use. This means you can use it however you wish without worrying about royalty payments.
Another benefit is that TensorFlow simplifies a task that was (before now) very complicated, even if you had ample experience in this area. It supports C++ and Python APIs, although you do not need a lot of complex coding to carry out tasks like preparing a neural network or programming a neuron.Â
Moreover, TensorFlow comes with broad hardware support. While we often undertake a deep learning training process on a Central Processing Unit (CPU), it can take a very long time, thanks to the large quantity of data and processes involved with it.Â
TensorFlow supports both CPUs and GPU (Graphical Processing Units), giving you greater flexibility during the development process. It may also compile faster than similar tools, meaning you can complete this task more quickly.
How Does TensorFlow Work?
Before you get started with TensorFlow, you need to ensure you have a baseline of knowledge at hand.Â
A good understanding of programming languages (such as PySpark) is essential in software development. We recommend Python to use TensorFlow most effectively.
Other programming languages (such as C++ and Java) are compatible but may not be as stable. Luckily, there are plenty of Python ML librariesOpens a new window to choose from nowadays.
You also need to have suitably in-depth knowledge of machine learning. Some mathematical knowledge will be useful, too, if you are trying to add or implement new features somewhere. A basic understanding of linear algebra and statistics will be relevant here.
Using TensorFlow involves manipulating something called a tensor (which is where the tool gets its name). A tensor is an n-dimensional vector or matrix that represents a specific kind of data. Vectors have one dimension, while matrices have two.Â
When tensors are interconnected, this allows computations within TensorFlow to take place. The tool uses the tensor to take an input, which then “flows†through a series of operations and pops out the other end.Â
TensorFlow’s computations take place within graphs, which are essentially a series of nodes. Each node represents a simple mathematical operation (like add, subtract, or multiply). Without this graph to act as a framework, TensorFlow cannot actually do anything, so it must be assembled properly.
Luckily, TensorFlow will provide a graph when you start creating an object in the TensorFlow tool. You can create your own graph if needs be. As previously mentioned, a graph’s processes can be executed on either a CPU or GPU, depending on your requirements.
Tensors are used in every computation that TensorFlow undertakes, allowing you to store complex data compactly. This is very important for deep learning, which, as we have established, is a key use for Tens
How Does TensorFlow Compare to Other Tools?
TensorFlow’s use of graphs means it may be unfamiliar to you if you are used to conventional computer programming. This is because TensorFlow handles data in a slightly different way. However, it is still important to understand it, especially if you are interested in the evolving role of humans with AIOpens a new window .
In conventional computer programming, we create a variable for anything that regularly changes. TensorFlow, on the other hand, allows you to store and manipulate data with a trio of programming elements. These are called constants, variables, and placeholders, and they are fairly self-explanatory even in this context.
As you may have worked out, constants do not undergo any changes once we create them. Variables, on the other hand, let us introduce new trainable parameters. Placeholders can have values assigned to them at a later date, and they can be used to feed in data from outside a specific model.Â
Is TensorFlow Popular?
Yes, TensorFlow has been adopted by several professionals in relevant industries. A 2020 Forbes profileOpens a new window highlighted that the number of developer job postings related to TensorFlow had steadily increased since its introduction.
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Forbes went on to highlight several other advantages of TensorFlow. These included its ubiquity (TensorFlow can be used on a wide variety of architectures) and suitability for everything from mobile phones to internet of things devices.Â
It also allows developers to manage the entire life cycle of an AI application. And its Google backing suggests it will continue to see significant investment going forward.
Just as business continuity software lets us plan for worst-case scenarios, understanding AI and machine learning now will help you embrace new technologies going forward.Â
In Brief
TensorFlow is an excellent choice for any developer interested in artificial intelligence and machine learning. In the first several years of its life, it has already proven its worth in fields like web search and voice recognition. The technologies that underpin these services are likely to grow in importance over the years to come.
However, TensorFlow is not something you can just pick up and start using. It requires an array of sophisticated skills to truly reap its benefits. We hope this article about TensorFlow for beginners puts you on the path to getting the most from it.Â
Have you used TensorFlow to build AI applications? What are your thoughts about the language? Let us know on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window .