What Is TensorFlow? (Components, Applications And Jobs)

Updated 9 July 2022

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TensorFlow helps developers solve challenging, real-world problems with machine learning (ML). Whether you are an expert developer or a beginner, TensorFlow makes it easy for you to build and deploy ML models. It opens a wide range of innovation opportunities and offers excellent jobs in growing industries. In this article, we answer, "What is TensorFlow?" and some frequently asked questions about it, explore its components and applications and outline some jobs that use TensorFlow.

What is TensorFlow?

If you are considering a career in technology, it may be helpful to understand the question to "What is TensorFlow?". TensorFlow is a free and open-source software library for machine learning (ML) and artificial intelligence (AI). It provides a collection of workflows to develop and train ML models using Python or JavaScript and quickly deploy them in the cloud, on a device or in the browser. Its artificial intelligence library allows developers to create large-scale multi-layered neural networks, allowing computer programs to recognise patterns and solve complex problems.

Though TensorFlow is helpful across a range of tasks, it focuses on the training and inference of deep neural networks. It can also help create algorithms to visualise objects and train a machine to recognise the object. TensorFlow has applications in a wide range of technologies, some of which are mentioned below:

voice recognition
image recognition
optical character recognition
sentiment analysis
deep neural network
self-driving cars
video processing

Components of TensorFlow

Here are some of the major components of TensorFlow:

Tensors: They are multidimensional data arrays with dynamic sizes to perform computations.
Variables: A variable represents a tensor whose value can be changed by running operations on it.
Graphs: The graph collects and describes all the series computations done during the training.
Nodes: Each node of the graph represents an instance of mathematical operations like addition, division or multiplication.
Placeholders: It sends data between your program and the TensorFlow graph.
Session: A session allows you to execute graphs or parts of a graph. It allocates resources and holds the actual values of intermediate results and variables.

How TensorFlow works

TensorFlow allows developers to create dataflow graphs that describe how data moves through a graph or a series of processing nodes. Each node in the graph represents a mathematical operation and each connection or edge between nodes is a multidimensional data array or tensor. TensorFlow provides all of this for the programmer through the Python language, which is easy to learn and work with and provides convenient ways to express high-level abstractions.

TensorFlow application in various industries

TensorFlow can use data to understand patterns and behaviour from large datasets and deploy various analysis models. Following are some example applications of TensorFlow:

Health care

The health care industry can use TensorFlow and AI imaging technologies to increase the speed and accuracy of interpretation for medical images. DermAssist, a free mobile application that allows users to take pictures of their skin and identify potential health complications. Automated billing and cost estimation tools for hospitals are also one area where TensorFlow can be helpful.

Education

Virtual learning platforms can use TensorFlow to filter out inappropriate chat messages in classrooms. An online learning platform can use TensorFlow to create a customised curriculum for each student. It can also help evaluate assessments and grade students at scale.

Social media

Social media platforms can use TensorFlow to rank posts by relevance to a user and display them in their feed accordingly. Sentiment analysis using TensorFlow can help organisations monitor talks about products and services and optimise their social media strategy to manage the brand's public image. Photos storing apps can use computer vision to understand the objects and people in photographs and automatically group similar photos or allow advance searches.

Search engine

Search engines use natural language processing (NLP) to understand the content of a webpage and decide its relevancy to a search term. TensorFlow can also help analyse enormous amount user behaviour data to use them as ranking signals. Search engines can use TensorFlow machine learning capabilities for pattern detections, which can help identify spam and duplicate content.

Retail

Using AI and TensorFlow machine learning can help a retail business forecast how many goods they would need on a particular day in response to their consumer demands. E-commerce platforms can use TensorFlow to understand their preference and provide personalised recommendations to their customers. A company selling spectacles can use TensorFlow to create an augmented reality experience for customers to test various spectacles on their faces.

Frequently asked questions about TensorFlow

Following are some questions you may have related to TensorFlow:

Is knowing Python necessary for working with TensorFlow?

Though TensorFlow supports lots of languages, the most commonly used one is Python, followed by JavaScript. TensorFlow also supports Swift, C, Go, C# and Java. Knowledge of Python is not necessary for working with TensorFlow, but Python makes it very easy to work with TensorFlow.

Can I use TensorFlow without Keras?

Yes, you can use TensorFlow without Keras. Keras provides a Python interface for artificial neural networks. It acts as an interface for the TensorFlow library, which makes working with TensorFlow more user-friendly.

Which is better between PyTorch and TensorFlow?

TensorFlow is far superior in terms of scalability and production models. It is ready for use on production, whereas PyTorch is easier to learn and work with, making it a better choice for passion projects and rapid prototyping. Compared to PyTorch, a significantly larger community supports TensorFlow. This makes it easier to access resources for learning TensorFlow and solutions to many problems.

What role does TensorFlow play in artificial intelligence?

Artificial intelligence adds capabilities to machines to mimic cognitive functions that associate with the human brain, such as learning and problem-solving. TensorFlow has a comprehensive, flexible ecosystem of tools, libraries and resources that help developers easily build and deploy AI-powered applications. The development of AI creates new opportunities to solve challenging, real-world problems.

What are the major alternatives to TensorFlow?

PyTorch, cognitive toolkit (CNTK) and MXNet are three major alternative frameworks to TensorFlow. Apache MXNet is an alternative option as the premier deep learning framework on AWS, which can scale almost linearly across multiple GPUs and multiple machines. CNTK handles many neural network jobs faster than TensorFlow and has a broader set of APIs.

Types of jobs that use TensorFlow

Here are some of the most common types of jobs that benefit from the TensorFlow knowledge:

1. Data scientist

National average salary: ₹8,48,646 per year

Primary duties: A data scientist collects, analyses and interprets both structured and unstructured data. Data scientists produce easily understandable insights using their programming, statistics and computer science expertise. They often use massive data sets to project information in order to discover answers to specific concerns.

2. Computer vision engineer

National average salary: ₹14,379 per month

Primary duties: Computer vision engineers use machine learning and computer vision technologies to solve real-world challenges. To execute arduous tasks, they require enormous amounts of data and statistics and supervised learning as part of computer vision tasks. They assist the data science team in the development, evaluation and optimisation of various computer vision and deep learning models for various uses.

3. Machine learning engineer

National average salary: ₹49,161 per month

Primary duties: Machine learning engineers are responsible for creating programs and algorithms that allow machines to function on their own. They may have a detailed understanding of computer science fundamentals like data structures, computer architecture and algorithms. Machine learning engineers may also have excellent mathematical and programming skills to compute and create high-level programs. They ensure programs run smoothly and identify issues to resolve.

4. Algorithm engineer

National average salary: ₹9,86,217 per year

Primary duties: Algorithm engineer focus on researching, performance testing, algorithms and writing. As an algorithm engineer, your duties include implementing the algorithms and regularly evaluating them to change as needed. You may also design, create and evaluate algorithms in natural language processing (NLP), ML and AI.

5. Deep learning engineer

National average salary: ₹14,664 per month

Primary duties: Deep learning engineers specialise in leveraging deep learning platforms for various types of artificial intelligence tasks. They use neural networks to create programming systems that function similar to the brain. Deep learning engineers create a system and design plans to figure out how to connect machines and programs.

Please note that none of the companies mentioned in this article are affiliated with Indeed.

Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.