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An Easy & Detailed Guide On What Is TensorFlow

Machine learning is a complicated drill, but enacting machine learning structures is far less scary than it previously was because of the machine learning framework like Google’s TensorFlow. This framework eases the process of obtaining the data, training models, serving anticipations, and refining future outcomes. In this article, we will have a detailed look at what is TensorFlow and how it operates. 

Formulated by the Google Brain team and primarily released to the audience in 2015, TensorFlow is an open-source library for numerical computation and large-scale ML. TensorFlow bundles together a range of deep learning and machine learning structures and also algorithms and makes them useful by way of relevant programmatic metaphors. It employs JavaScript or Python to offer an easy front-end API for constructing applications while implementing those apps in high-performance C++. Let us now start with tensor flow basics.

History Of TensorFlow

Let us begin this article on what is TensorFlow by knowing about his history. Many years ago, deep learning began to exceed all other ML algorithms when offering extensive data. Google has found it could utilize these deep neural networks to upgrade its services:

  1. Google Search Engine
  2. Gmail
  3. Photos

They constructed a framework known as TensorFlow to allow researchers and also developers to operate together in an AI structure. Once it is approved and is scaled, it enables a lot of people to employ it. 

OIT was initially launched in 2015 while the first stable version came in 20170. It is an open-source platform under Apache Open Source Licence. The users can use it, modify it, and reorganise the revised variant for free without paying anything to Google.

Components Of TensorFlow

To understand what is TensorFlow, you should first know about the various components of TensorFlow. So without any further delay, let us begin with this guide.


The name TensorFlow is obtained from its basic framework, “Tensor.” A tensor is basically a vector or matrix of n-dimensional that portrays all forms of data. Every value in a tensor holds a similar type of data with a relevant shape. The shape of the data is the dimension of the matrix or even an array. 

A tensor can be developed from the outcome or the input data of a computation. In this data analytics framework, all tensor flow uses are conducted within a graph. The group is a set of calculations that occurs successively. Every transaction known as an op node is linked. 


TensorFlow makes use of a graph structure. The chart assembles and also illustrates all the computations done at the time of the training.


  • It was fixed to operate on various GPUs and CPUs and mobile OS. 
  • The portability of the graph enables it to conserve the computations for present or later use. The graph can further be saved as it can be implemented in the future. 
  • All the computations in that graph are made by connecting tensors together. 

You can consider the following expressions a= (b+c)*(c+2)

You can break the functions into components as given below:





A session can implement the operation from the graph. To stuff the graph with values of a tensor, we are required to open a session. Within a session, we must run an operator to form a result. 

Why Is TensorFlow Famous?

This data analytics framework is the better library for all as it is easily accessible to all. TensorFlow library accommodates varied API to formulate a scale deep learning frameworks such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN). 

This framework is based on graph computation as it can enable the developer to formulate the creation of the neural network along with Tensorboard. His tools enable debugging the program. It operates on GPU or CPU.

Applications Or Use Cases Of TensorFlow

TensorFlow offers great operations and services when compared to other famous deep learning frameworks. TensorFlow is used to formulate a large-scale neural network with many layers.

It is primarily used for machine learning or deep learning issues such as Perception, Classification, Understanding, Discovering Predictions, and Creation.

Voice Or Sound Recognition

Sound and voice recognition applications are the most popular use cases of deep learning. If the neural networks have proper input data feed, neural networks are capable of evaluating audio signals. 

For Instance:

  • Voice Recognition is employed in the IoT (Internet of Things), security, automotive, and UI/UX. 
  • Sentiment Analysis is primarily used in customer relationship management (CRM). 
  • Flaw Detection (engine noise) is primarily used in Aviation and automotive. 
  • Voice Search is primarily used in customer relationship management (CRM).

Image Recognition

Image recognition is the first application that made machine learning and deep learning popular. Social Media, Telecom, and handset manufacturers, motion detection, image search, photo clustering, and machine vision. 

For instance, image recognition is used to identify and recognize objects and people in the form of images. This is used to evaluate the context and content of any image. 

In the case of object recognition, TensorFlow allows to classification and identify arbitrary objects within larger images. This is also availed in engineering applications to recognize shapes for the purpose of modeling and by Facebook for photo tagging. 

For instance, deep learning utilizes TensorFlow for evaluating thousands of photos of cats. So a deep learning algorithm can learn to identify a cat as this algorithm is used in finding general features of objects, peoples, and animals.

Time Series

Deep learning is using Time Series algorithms for examining the time series data for extracting meaningful statistics. For instance, it has employed the time series to anticipate the stock market. 

A recommendation is the most common use scenario for Time Series. Amazon, Facebook, Google, and Netflix are employing deep learning for the suggestion. So, the algorithms of deep learning is used to evaluate customer activity and compare it to numerius of other users to evaluate what the user may wish to buy or watch. 

For instance, it can be used to suggest us TV shows or even movies that people wish to see based pn movies and TV shows already watched.

Video Detection

The deep learning algorithm is jused for the purpose of video detection. It is used in the case of motion detection, real time threat detection in security, gaming, airports, and UI/UX areas. 

For instance, NASA is formulating a deep learning network for objects clustering of orbit and asteroids classifications. So, it can anticipate and classify NEOs (Near Earth Objects).

Text Based Applications

Text based applications is also a famous deep learning algorithm. Sentimentals analysis, threat detection, social media, and fraud detection are the instanes of Text-based applications. 

For instance, Google Translate supports more than 100 languages.

Features Of TensorFlow

This article on what is TensorFlow would be incomplete if we do not talk about the features of this popular tool. TensorFlow has a synergistic multiplatform programming interface which is reliable and scalable in comparison to other deep learning libraries which is available.  These features of TensorFlow will narrate us about the popularity of TensorFlow.

Responsive Construct

We can visualize every part of the graph which is not an option while utilizing SciKit or Numpy. To develop a deep learning application, primarily there are two or three components that are needed to formulate a deep learning application and require a programming language.


It is among the important TensorFlow features as per its operability. It offers modularity and parts of it which we wish to make standalone.

Easily Trainable

It can be trained conveniently on CPUs and in case of GPUs in distributed computing.

Parallel Neural Network Training

TensorFlow provides to the pipeline in the sense that we acn educated numerous neural networks and several GPUs which makes the frameworks very effective on large scale mechanisms.

Large Community

Google has developed it, and there already is a big team of software engineers who operate on stability enhancement continuously.

Open Source

The best thing about the machine learning library is that it is open so that any individual can it as much as they connection with the internet. So, individuals can manipulate the library andcome up with a great variety of useful items. And this has become another DIY space which has a huge forum for people who plan to start with and also those who find it difficult to work with.

Feature Coulmns

TensorFlow has feature columns which could be identified of as intermediates between estimators and raw data, as per bridging input data with our framework.


We come to the colnclusing lines on what in TensorFlow? In recent times, this software has become the popular data learning library. Any deep learning framework like a RNN, CNN, or basic artificial neural network may be constructed using TensorFlow. 

Startups, academics, and major corporations are the most common users of thai software. This is used in digitally all the products of Google that includes Gmail, Photos, and Google Search Engine.

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