What is TensorFlow? How does it work? Components and benefits?

August 04, 2021 | Louis Garrick | Online Courses

What is TensorFlow? How does it work? Components and benefits?

Today, the world is all about machine learning, deep learning, and artificial intelligence. These are complex subjects. Understanding them is one thing and implementing them is a different story. To solve these complexities, a software has been developed called TensorFlow. 

What is TensorFlow? How does it work? Components and Benefits
What is TensorFlow? How does it work? Components and Benefits

What is TensorFlow?

TensorFlow is a machine learning software. Google developed this intending to help and aid mankind in their quest for machine learning. TensorFlow aids in the data collection, making predictions, analysis, and training models. Therefore, people love and use it worldwide. 

In other words, we can say it is a library. Just like a library full of books, it is full of numerical computation and machine learning. It further covers deep learning as well. Due to its prevalence and efficiency, many organizations use it now. Anyone can learn TensorFlow, you can take a TensorFlow course or certification and start using it.

What is TensorFlow
What is TensorFlow

How does TensorFlow work?

TensorFlow works like any other software. It is a python-based software which means that programmers developed it through code. Python is a very convenient programming language. Although, it performs functions using C++ language. Because it is python based, it can run neural networks for image recognition, national language processing, model sequencing, and word embedding. It also allows developers to create graphs and solve multiple mathematical problems. The architecture of TensorFlow is very flexible, it facilitates computation across different platforms and servers. 

How does TensorFlow work
How does TensorFlow work

You can use TensorFlow on any device, iOS, Android devices, CPUs, and GPUs. There are two versions of TensorFlow:

1. TensorFlow

2. TensorFlow version 2.0

Components of TensorFlow

There are two main components of TensorFlow. The first one corresponds to the name that i.e., tensor itself, and the second one is ‘graphs’.


A tensor is a matrix that represents all types of data. We can create tensors through inputs or computation. All the work on TensorFlow is done through tensors. 


TensorFlow is based on graphs. All the computation and operations are carried by using graphs. These graphs gather all the data and present it to the user. They have many advantages. They are easy to interpret and portable. Moreover, we can use them on multiple servers.

Advantages of the TensorFlow

People all over the world use TensorFlow because of its advantages. Let us see the benefits for ourselves.

Advantages of the TensorFlow
Advantages of the TensorFlow

Open-source library

TensorFlow is an open-source platform which means that every user can have access to the TensorFlow data source. Moreover, it is always ready for the development of any system. 

Better visualization tools

One of the main advantages is better visualization. It has a TensorBoard visualization suite. It enables you to view and inspect your graphs. You can effortlessly evaluate your graph construction and appearance together.


Google TensorFlow is very efficient. It gives an extraordinary performance and fast response. Furthermore, the developers frequently come up with new and advanced updates and features. These features help the software to stay up-to-date and unbeatable. 


The compatibility of TensorFlow is unmatched. It can work in many different client languages like C++, JavaScript, Python, Go, and Swift. This allows the user to choose a language they are familiar with. 

Architectural TPU

The TensorFlow software has its processing unit. It is a better and more convenient option than GPU or CPU. The models which we build on TensorFlow can be easily deployed on the cloud. 

Parallel Neural networking

TensorFlow allows parallel neural network training. It enables you to choose any architecture thus, making different models available to use. 

Compatible with Keras

TensorFlow is now compatible with Keras. This is possible due to its new update. The users can code high-quality sections using TensorFlow. 

Applications of Tensor flow

TensorFlow has wide applications due to its command of Deep learning and machine learning. It has a wide range of applications like,

1.Image and video recognition

2. Self-driving cars

3.Deep neural networks

4.Mobile image and Video processing

5.Sentiment analysis 

6.Text summarization


To summarize all this, I would say that TensorFlow deserves all the hype it receives. It has many advantages and people use it worldwide. Moreover, Google aims to make it better and better, therefore, other platforms cannot beat it. If you are looking to invest in this software, you should. It may not be the ideal platform. However, the benefits are much more than the drawbacks. You can watch any TensorFlow tutorial, it will help you use them efficiently.