TensorFlow Tutorial for Beginners

TensorFlow is a software library created by Google. It is open-source and primarily used to implement deep learning and machine learning systems. In essence, this library allows a computer to learn spotting patterns and make the best possible decisions from them. 

At its very core, TensorFlow is all about dataflow programming and leverages different optimisation techniques to make mathematical calculations easier and more intuitive. Some key features that make TensorFlow a supremely efficient tool include: 

  • Works efficiently with mathematical expressions, especially those involving n-dimensional arrays and vectors. 
  • Supports deep neural network concepts with ease. 
  • Provides GPU as well as CPU computing abilities. 
  • Offers scalability of computations across vast data sets and different machines and systems. 

Together, these features make TensorFlow the perfect framework for machine intelligence at a production scale.

This TensorFlow tutorial will help you set up and install TensorFlow. We’ll also talk in-depth about its use cases, features, advantages, limitations, and more! 

Installing TensorFlow

In this tutorial, we will be looking at the TensorFlow Python API that works with Python 3.3+. The Linux Only version of this requires the Cuda Toolkit and cuDNN. 

First, we will begin by using the Conda package manager to install TensorFlow onto our system. That way, we can separate multiple environments on one system. Learn more about installing Conda from here.

Once you have Conda installed and in place, you can quickly create the environment to be used while TensorFlow is being installed and used. After installing Conda, we can make the environment that we will use for TensorFlow installation and use. The following line of code is essential for setting up the environment with additional important libraries like NumPy etc., which are crucial for working with TensorFlow: 

conda create –name TensorflowEnv biopython

To make things simpler for you, you can install biopython, like we have above, instead of just installing NumPy. Biopython in itself includes NumPy and a few other vital packages that will be required while working with TensorFlow. Plus, you can always install packages that you need using the pip install or conda install commands. 

Now that our environment is created, we need to activate the domain to begin working. Once we have activated the environment, we will start using different packages it provides without mixing them with other globally installed packages. Here is the line of code that can be used for activating the conda environment:

source activate TensorFlowEnv 

One thing to note here is that the pip installation tool is an integral part of the Conda environment. Using that, we will be able to install the TensorFlow library. But before doing that, it is a good idea to update pip to the latest version using the following line of code: 

pip install –upgrade pip

Now, simply run the below-mentioned command to quickly install TensorFlow: 

pip install tensorflow

The final download of TensorFlow may take several minutes, so be patient. Meanwhile, let’s move ahead and try to understand what Tensors mean. 

What are Tensors?

As the tool’s name would suggest, TensorFlow provides primitives for defining functions using Tensors and computing their differentiations. At this point, it is crucial to take a moment and see what Tensors are. 

Simply put, tensors are just higher dimensional arrays that can be utilised to represent a massive amount of data in the form of numbers in computer programming terms. NumPy and several other n–d array libraries are available, too, but TensorFlow stands apart by providing methods to create tensor functions that let it automatically find the derivatives. 

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With that settled, let’s now look at some more uses of TensorFlow.

Use Cases of TensorFlow

TensorFlow makes it easier to build decision trees or k-nearest neighbours and other machine learning algorithms for the best result. The below image represents the entire ecosystem of TensorFlow: 


As you can see from the above image, TensorFlow integrates seamlessly and provides dependencies that include Cpp, Python, GPU processing and can be used to integrate with dockers as well. 

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Now, let’s move on to understand the concept of a TensorBoard in TensorFlow. 

TensorBoard in TensorFlow

TensorBoard is simply a collection of visualisation tools provided by TensorFlow for creating seamless, intuitive, and easy-to-understand graphs and visualisations. Using this board, you can also plot different quantitative metrics about the graph and provide it additional information such as images to pass through it.

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Operation in TensorFlow

TensorFlow runs on several platforms, and the installation method it follows is more Linux-only, which is slightly tedious than CPU-only installations. However, you can install it using the conda or pip environment. In terms of operations, the TensorFlow application goes well beyond deep learning and machine learning and supports all the different and essential aspects of machine learning like reinforcement learning, supervised learning, and such. These techniques are important for developing goal-oriented applications like helping a robot navigate uneven terrain, winning at video games, etc. 

Applications of TensorFlow

TensorFlow comes with many applications that can be used by engineers and scientists alike. To begin with, TensorFlow makes it easy to explore different machine learning strategies like google translate, text summarisation, image recognition, sentiment analysis, and much more. 

The software is trusted and employed by industry leaders and giants like Uber, IBM, Dropbox, Airbnb, Facebook, Google, Instagram, and many more. So wherever you need robust machine learning or deep learning algorithms or systems capable of learning independently, you will find TensorFlow in action. 

Tensorflow – Features, Advantages, and Limitations

Now that we understand TensorFlow’s basic operations and applications, let’s dive deeper and discuss TensorFlow’s features, advantages, and disadvantages as a software programming library. 

TensorFlow is feature-rich and provides APIs for C++, MATLAB, Python along with a wide array of other programming languages support. With each passing day, as machine learning and the tools behind it get more sophisticated, TensorFlow is improving, too. In a recent Summit, TensorFlow introduced a javascript library for working with machine learning models – called tensorflow.js. You can check it out in more detail at Here you can see real-time changes. How cool is that!

Now let’s look at a few important advantages of using TensorFlow in your machine learning projects and endeavours: 

  • The TensorBoard provided with TensorFlow is superb for creating simple and quick visualisations to convey your findings to a large population of people. 
  • TensorFlow is inherently platform-agnostic. So, it can be used with different platforms and languages as per your need. 
  • Training TensorFlow models is easy, both on GPU and CPU distributed computing. 
  • TensorFlow brings with it auto differentiation abilities. That way, it benefits gradient-based learning algorithms. 
  • It has support for asynchronous computation, queues, and threads. 
  • Last but not least, the library is entirely open-source, allowing for more modifications and improvements as per the developers’ choices. 

Now, let’s check out some limitations or drawbacks of TensorFlow. These drawbacks are being worked on as you read this, and with time, they will be resolved by the developers. 

  • TensorFlow comes with GPU memory that might conflict with Theano if both are important in the same application scope. 
  • TensorFlow lacks any support for OpenCL. 
  • To properly work with TensorFlow, you need understanding and knowledge of calculus, algebra, and not to forget – machine learning.


In this tutorial, we saw what TensorFlow is, how it is installed, what Tensors are, along with different applications, features, benefits, and limitations of using TensorFlow. If you have read this far, chances are, you would want to know more about working with TensorFlow. 

This introductory tutorial was written to give you a basic understanding of TensorFlow as a tool. Now, with this knowledge by your side, you will be able to dive deeper into the world of creating and training machine learning models using the open-sourced TensorFlow library. 

If you still feel stuck, check out our Advanced Certification in Machine Learning and Cloud, where we take you from the very basics and help you develop your models and systems. The course is offered in collaboration with IIT-Madras and is designed to help both beginners and people with some experience. 

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What should I know before working with TensorFlow?

It is recommended that you understand advanced calculus, linear algebra, and machine learning concepts before you get to work with the TensorFlow library.

Is TensorFlow a beginner-friendly tool?

Being end-to-end open-sourced with a vast network of peers to support you, TensorFlow makes it easy for beginners to get started in the journey of creating ML models.

Is TensorFlow an API or library?

TensorFlow is a software programming library.

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