TensorFlow 101: What Is TensorFlow and How It Works?

TensorFlow 101: What Is TensorFlow and How It Works?

Understanding the fundamentals of TensorFlow is essential if you want to work in AI. In this article, we will explain what TensorFlow is and how TensorFlow works.
Alan Kilich
3 minutes

TensorFlow 101: What Is TensorFlow and How It Works?

AI is getting more and more "intelligent" by the day. We can easily see this in AI-related systems as well. Of course, it is clear that while these systems are being created, the infrastructure under them develops simultaneously. TensorFlow, one of the AI ​​and its resulting Deep Learning libraries, is one of these.

In fact, the development of TensorFlow, an open-source AI toolkit that enables the use of data flow graphs to create models, has been fueled by the emergence of artificial intelligence (AI) and deep learning pretty much.

Understanding the fundamentals of TensorFlow is essential if you want to work in AI. In this article, we will explain what TensorFlow is and how TensorFlow works.

What Is TensorFlow?

A complete open-source framework for building machine learning applications is called TensorFlow. It is a symbolic math toolkit that carries out several operations targeted at deep neural network training and inference using dataflow and differentiable programming. It enables programmers to build machine learning applications by utilizing a range of instruments, frameworks, and community assets.

Google's TensorFlow is now the most well-known deep learning library in the world. All of Google's products include machine learning to enhance the search engine, translation, picture captioning, or recommendations.

In order to provide the greatest experience for users, Google aims to leverage machine learning to benefit from its enormous datasets. Machine learning is used by three different groups:

  • Scholars and Students
  • Data Scientists
  • Programmers & Software Developers

They can all work together and become more effective by using the same set of tools.

Tensor Flow was made to scale since Google has the most powerful computer in the world and plenty of data. The Google Brain Team created the TensorFlow library to accelerate machine learning and deep learning research.

It features a number of wrappers in different languages like Python, C++, or Java, and it was designed to work on a variety of CPUs, GPUs, and even mobile operating systems.

How Does TensorFlow Work?

TensorFlow accepts inputs as a multi-dimensional array called Tensor, allowing you to create dataflow graphs and structures to specify how data goes through a graph. It enables you to create a flowchart of the operations that can be carried out on these inputs, with the output appearing at the other end.

Architecture of TensorFlow

There are three components that create the TensorFlow architecture:

  • Interpretation of the data
  • Creating the model.
  • Model training and Estimation

TensorFlow is so named because it accepts input in the form of multi-dimensional arrays, or tensors. You can create a "graph" (also known as a flowchart) of the actions you intend to carry out on that input. The input enters at one end, passes through this system of many processes, and finally exits at the other end as the output.

The reason it is named TensorFlow is that the tensor enters, travels through a number of computations, and finally exits.

Where Can TensorFlow Be Run?

The hardware and software requirements for TensorFlow can be divided into two groups:

In the development phase, you train the mode. Typically, you use your desktop or laptop for training.

After training is complete, TensorFlow can be used on a variety of platforms in the run phase or inference phase. You may use it on:

  • Windows, 
  • macOS, 
  • Linux 
  • Cloud as a web service
  • iOS 
  • Android

Once you have the trained model, you can run it on a separate machine after training it on many machines.

There is a lot of matrix multiplication involved in deep learning. Because TensorFlow is developed in C++, it does matrix multiplication extremely quickly. TensorFlow can be accessed and controlled by other languages, mainly in Python, despite the fact that it was implemented in C++.

The TensorBoard is a crucial component of TensorFlow. TensorFlow can be graphically and visually tracked with the TensorBoard.

Components of TensorFlow


The term TensorFlow is directly taken from the name of its primary framework: Tensor. Tensors are used in every computation of Tensorflow. A tensor is an n-dimensional vector or matrix that may represent any kind of data. A tensor's values all have the same data type and known (or at least partially known) form. The dimensions of the matrix or array are determined by the geometry of the data.

A tensor might start as input data or as the output of a calculation. All actions in TensorFlow are carried out inside of a graph. The graph is a collection of calculations that happen one after another. Each operation is referred to as an "op node," and they are all interconnected.

The operations and relationships between the nodes are shown in the graph. It does not, however, show the values. The tensor, or method of supplying data to the operation, is the edge of the nodes.


TensorFlow uses a framework built on graphs. All of the series of computations made throughout the training are collected and described in the graph. The graph has several benefits, including:

  • It was made to work with mobile operating systems, multiple CPUs, or GPUs.
  • The graph's mobility enables the preservation of calculations for use now or in the future. The graph may be saved for later execution.
  • By combining tensors, the graph's calculations are all performed.
  • There are nodes and edges in tensors. The node does the computation and generates the output for the endpoint. 
  • The relationships between nodes' input and output are explained by their edges.

Why is TensorFlow so well-liked?

The finest library is TensorFlow since it was designed to be user-friendly for everyone. The Tensorflow library uses a variety of APIs to build deep learning architectures like CNNs and RNNs at scale. TensorFlow, which is based on graph computation, enables the developer to use Tensorboad to see how a neural network is being built. This tool is useful for software debugging. Finally, Tensorflow is designed for large-scale deployment. The CPU and GPU power it.

Comparatively speaking, Tensorflow is the most used deep learning framework on GitHub as well.

The algorithms that TensorFlow supports are listed below. TensorFlow currently offers an integrated API for:

  • Deep learning classification: tf.estimator.DNNClassifier
  • Deep learning wipe and deep: tf.estimator.DNNLinearCombinedClassifier
  • Booster tree regression: tf.estimator.BoostedTreesRegressor
  • Boosted tree classification: tf.estimator.BoostedTreesClassifier
  • Linear regression: tf.estimator.LinearRegressor
  • Classification:tf.estimator.LinearClassifier

What's the next step?

Deep learning and machine learning model implementation have been more simpler thanks to TensorFlow. Even though TensorFlow programming is only a small portion of the complex field of deep learning, it is one of the most essential parts.

If you are looking for an AI-Based Computer Vision application example, take a look at Cameralyze’s solutions such as face recognition or object detection, and see what can be done with the library of TensorFlow.

Click here to start your free membership and have a chance to discover Cameralyze CV applications now!

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