TensorFlow is a free and open-source software library for machine learning and artificial intelligence.

It has developed by Google primarily for deep learning applications. Before we dive into the TensorFlow framework we must know about deep learning and why we use it. Here we consider that you are familiar with deep learning. So basically, Deep learning is a subset of machine learning. It works on the structure and functions similarly to the human brain. It learns from unstructured data and uses complex algorithms to train a neural network model.

Some libraries are used for performing machine learning and deep learning programming. Some of the most common libraries are Keras, Theano, TensorFlow, Torch etc.

And in this blog, we will discuss TensorFlow.

TensorFlow is an end-to-end machine learning platform. TensorFlow was originally developed for large numerical computations without keeping deep learning in mind. However, it proved to be very useful for deep learning development as well, and therefore Google open-sourced it. TensorFlow accepts data in the form of multi-dimensional arrays of higher dimensions called tensors.

TensorFlow works based on data flow graphs that have nodes and edges. As the execution mechanism is in the form of graphs, it is much easier to execute TensorFlow code in a distributed manner across a cluster of computers while using GPUs.

Why TensorFlow?

TensorFlow Offers Both C++ and Python APIs

Before the development of libraries, the coding mechanism for machine learning and deep learning was much more complicated. This library provides a high-level API, and complex coding isn’t needed to prepare a neural network, configure a neuron, or program a neuron. The library completes all of these tasks. TensorFlow also has integration with Java and R.

TensorFlow Supports Both CPUs and GPUs Computing Devices

Deep learning applications are very complicated, with the training process requiring a lot of computation. It takes a long time because of the large data size, and it involves several iterative processes, mathematical calculations, matrix multiplications, and so on. If you perform these activities on a normal Central Processing Unit (CPU), typically it would take much longer.

Graphical Processing Units (GPUs) are popular in the context of games, where you need the screen and image to be of high resolution. GPUs were originally designed for this purpose. However, they are being used for developing deep-learning applications as well.

One of the major advantages of TensorFlow is that it supports GPUs, as well as CPUs. It also has a faster compilation time than other deep learning libraries, like Keras and Torch.

How TensorFlow Works

TensorFlow allows you to create dataflow graphs that describe how data moves through a graph. The graph consists of nodes that represent a mathematical operation. A connection or edge between nodes is a multidimensional data array. It takes inputs as a multi-dimensional array where you can construct a flowchart of operations that can be performed on these inputs.

What can we do using TensorFlow?

Prepare data: Data is the first thing when we think about an ML project. After the collection of data, we must prepare the data for further implementation.

TensorFlow offers multiple data tools to help consolidate, clean and preprocess data at scale:

  • Standard datasets for initial training and validation

  • Highly scalable data pipelines for loading data

  • Preprocessing layers for common input transformations

  • Tools to validate and transform large datasets

Additionally, responsible AI tools help you find and remove bias in your data to produce fair, ethical outcomes from your models.

Build ML models: Explore an entire ecosystem built on the Core framework that streamlines model construction, training, and export. TensorFlow supports distributed training, immediate model iteration and easy debugging with Keras, and much more. Tools like Model Analysis and TensorBoard help you track development and improvement through your model’s lifecycle.

Deploy model: TensorFlow provides robust capabilities to deploy your models on any environment - servers, edge devices, browsers, mobile, microcontrollers, CPUs, GPUs, and FPGAs. TensorFlow Serving can run ML models at production scale on the most advanced processors in the world, including Google's custom Tensor Processing Units (TPUs).

Implement MLOps: The TensorFlow platform helps you implement best practices for data automation, model tracking, performance monitoring, and model retraining.

Using production-level tools to automate and track model training over the lifetime of a product, service, or business process is critical to success. TFX provides software frameworks and tooling for full MLOps deployments, detecting issues as your data and models evolve.

Where Can Tensorflow Run?

TensorFlow requirements can be classified into the development phase (training the model) and the run phase (running the model on different platforms). The model can be trained and used on GPUs as well as CPUs. Once the model has been trained, you can run it on:

  • Desktop (Linux, Windows, macOS)

  • Mobile devices (iOS and Android)

  • Cloud as a web service

Here are the main components of TensorFlow:


Tensor forms the core framework of TensorFlow. All the computations in TensorFlow involve tensors. It is a matrix of n-dimensions that represents multiple types of data. A tensor can be the result of a computation or it can originate from the input data.


Graphs describe all the operations that take place during the training. Each operation is called an op node and is connected to the other. The graph shows the op nodes and the connections between the nodes, but it does not display values.


By this official link of TensorFlow, you will directly land on the installation page where a step-by-step guide is provided to install TensorFlow in your system.

Readers today this much only. We will meet in our next blog.

Did you find this article valuable?

Support TechLearn India by becoming a sponsor. Any amount is appreciated!