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PyTorch vs TensorFlow: Deep learning comparison 2023

Jan 19, 23  |  Bakhtain Afzal

Now that driverless cars and realistic conversations with Alexa are a dream come true - Google Translate is originating a whole another phase of revolution; Deep Learning is modernizing and revamping the futuristic possibilities.

Nevertheless, Deep learning networks are everywhere we look and, in fact, are better than humans at numerous things like opponent teams in FIFA. It's all possible through harnessing state-of-the-art accuracy. Lately, Deep Learning has been getting enormous attention for all good reasons. It does everything that was once restricted to only humans.

Behind every mind-boggling tech innovation, Artificial intelligence and Machine learning are the key players. Millions of businesses are already leveraging ML frameworks, such as TensorFlow and PyTorch, for supporting a smooth workflow for generating impressive year-end revenues. 

In this article, we’ll take a look at two popular frameworks, their features, such as PyTorch dataloader, and compare them: PyTorch vs TensorFlow and their role in deep learning.

Key Takeaway 

  • PyTorch is an open-source framework for machine learning (ML) based on Python and the Torch library.
  • TensorFlow is an open-source framework for running machine learning, deep Learning, and other statistical and predictive analytics workloads.
  • A TensorFlow example helps you use best practices for automating data, tracking models, checking their performance, and retraining models. It's important to use production-level tools to automate model training and monitor product life, service, or business process.
  • PyTorch uses immediate execution, making it an accessible framework for testing and debugging a program. 
  • TensorFlow's graphical approach provides better data visualization. 
  • The use of TensorBoard PyTorch makes it easy to fix node bugs. 
  • TensorBoard makes it easier to fix the neural network without looking at the whole code.
  • When it comes to “is PyTorch faster than Numpy” Pytorch is almost 15 times faster.

What Is Deep Learning?

Machine Learning sows the seeds of Deep Learning - the process of a computer model imitating what comes naturally to humans - Siri, GPT3 and Google Translate are all examples of Deep Learning.

Due to DL's technologically advanced algorithm, it can even exceed a standard human-level performance, making it almost impossible to detect as a computerized action. Provided that DL frameworks, like TensorFlow and PyTorch, assist in automating task performance and streamlining the workflow.

What is PyTorch?

PyTorch is an open-source Machine Learning (ML) framework based on Torch. PyTorch framework or library is an open-source machine learning library used for building Deep Learning neural networks, which are written in Lua.

The purpose of PyTorch is to streamline procedures during the project prototyping and deployment stage. Its popularity comes from its ability to simplify the development of artificial neural networks. Most data scientists use the PyTorch framework or library to research and deploy AI applications, which are proven to revolutionize digitalization.

How Does PyTorch Work?

PyTorch's unique algorithmic features, e.g. dynamic graph computation, are due to its Pythonic nature. It enables data scientists and neural network debuggers to perform real-time testing of a portion of their code rather than testing the complete final code.


Here are the following features that PyTorch provides:

Automatic Differentiation

This technique assists in the development and training of neural networks. It numerically computes derivatives of an operation by making backward passes in a neural network. 


TorchScript, a PyTorch example , is a production environment that enables seamless transitions between modes. This flexibility catalyzes operation speed and supports an ease-of-use.

Tensor Computation 

Tensors are a PyTorch example that are generic n-dimensional arrays used for arbitrary numeric computation. And this process is accelerated by Graphics Processing Units (GPUs). These structures support operation and manipulation with APIs.


Modules are the building blocks of stateful computations, representing primary neural networks. A module can comprise of different parameters and modules. 


A variable is wrapped around a tensor to hold a gradient. It represents a node in a computational graph.

What is TensorFlow?

TensorFlow is an open-source end-to-end framework developed by Google. It was launched in 2015, and it provides documentation and training support. As well as that, it comes with various deployment options and cross-platform accessibility. 

This math library is used for the training of neural networks. Meanwhile, when comparing PyTorch vs TensorFlow , TensorFlow is best suited for complex dataflow programming operations concerning numerous tasks. Additionally, it comes with multiple abstraction levels for creating and training models.

How Does TensorFlow Work?

TensorFlow enables the creation of dataflow graphs and structures that represent data, i.e., a graphical flow of nodes. At the same time, graphical nodes represent mathematical operations. Also, an edge between nodes indicates a tensor, i.e., a multidimensional array.


The following are the features of TensorFlow: 


TensorFlow, an open-source ML library, allows general usage if there's an active internet connection. In the wake of this usage flexibility, users brainstorm new ideas for streamlined operations.

Responsive Construct

With TensorFlow, you can easily visualize every graphical detail. On the other hand, frameworks like SciKit or Numpy don't offer this feature. 

Parallel Neural Network Training

This Deep Learning framework offers a pipelined structure, i.e., you get the ability to train multiple neural networks and GPUs.

Statistical Distribution

The library comes with various distribution functions. You get Beta, Uniform, Bernoulli and Gamma. These functions play a significant role in specific approaches, such as the Bayesian model.

Layered Components 

TensorFlow comprises various functions such as tf.contrib.layers. This produces a layered operation of biases and weights with dropout, convolution layer, etc. These layered components are a prime Tensorflow example that aids the efficient solving of optimization problems during a numerical analysis.

TensorFlow vs PyTorch

PyTorch vs TensorFlow is often under debate, as both are Deep Learning frameworks with the same aim, i.e., automation of workflow operations. Despite both serving the same user base, TensorFlow is popular among tech giants and major business corporations. 

However, when referring to TensorFlow vs PyTorch , the PyTorch framework or library offers more benefits over TensorFlow. 

  • A PyTorch example is its dynamic definition of computational graphs. 
  • Contrarily, a TensorFlow example is the ability to manipulate dynamic graphs.

TensorFlow offers a steeper learning curve, unlike PyTorch, due to its Pythonic nature. 

As an overview of the difference between PyTorch and TensorFlow, TensorFlow is a low-risk option better suited for projects that require scalability and production models. 

On the other hand, PyTorch offers more utility and ease of use. Which increases its preferability for research and prototype creation.

What Is Cross Entropy Loss PyTorch?

Cross entropy loss PyTorch is the most common loss function and a PyTorch example used for neural network training. Cross entropy loss PyTorch is a metric used for measuring a model's efficiency in ML platforms. This loss is measured in binary digits, i.e., 0 and 1. Meanwhile, 0 signals a perfect model. And the goal is to make your model the closest to "0". 

Conclusively, cross entropy loss PyTorch measures the difference between the predicted distribution and an ML model's discovered probability distribution.

What Is a PyTorch Dataloader?

As the name suggests, PyTorch Dataloader is a class of data loading utilities. Its purpose combines a sampler and a dataset to offer an iteration. 

The PyTorch Dataloader supports both iterable-style and map-style datasets. 

What is TensorBoard PyTorch?

A TensorBoard PyTorch is a visualization toolkit used for Machine Learning experimentation. It allows the visualization and tracking of metrics like loss and accuracy.

Currently, TensorBoard PyTorch supports 5 visualizations. 

  • Scalars 
  • Images 
  • Audio 
  • Histograms 
  • Graphs 

Is Tensorflow Better Than Pytorch?

TensorFlow is a highly effective and developed deep-learning library with multiple choices for high-level model creation and excellent visualization capabilities. It comes with cross-platform support and includes deployment options suitable for production.

On the other hand, the main difference between PyTorch and Tensorflow is that PyTorch is a comparatively new and Python-friendly framework with a more active community.

When comparing TensorFlow vs Pytorch, PyTorch is more efficient, whereas TensorFlow offers usage-flexibility. It allows users to perform high-level operations and ideate new operational ideas. 

As a highlight of the difference between PyTorch and Tensorflow , TensorFlow is best suited for high-level functions; meanwhile, PyTorch offers technological ease and simplification for users that look forward to streamlining their business workflow through automation. 

What Are the Most Popular Deep Learning Frameworks?

Machine learning and artificial intelligence are no longer buzzwords. In recent years, the number of businesses using ML for data volume interpretation has increased tremendously. Deep Learning is essential for a smooth pipeline flow for data scientists and engineers globally. Today, we have a list of frameworks, like TensorFlow vs PyTorch, that enable the creation of tools that provide a higher level of abstraction and simplify of complex programming problems. 

Here are the top 5 Deep Learning frameworks that can help solve your business challenges.


Undoubtedly, TensorFlow is the most popular Deep Learning framework. Other than wrapper libraries, a TensorFlow example, facilitates the creation of deep learning models in Python, C++, and R. Also, it's compatible  with desktop and mobile platforms. As a result, TensorFlow is compatible with both desktop and mobile platforms.


PyTorch is based on Lua and is a top choice among the industry leaders such as Google, Twitter and Facebook. It uses CUDA and C/C++ libraries which speed up processes resulting in project scalability. And, this is the main difference between PyTorch and TensorFlow.

Its architecture serves as the base for deep modeling processes, making it more transparent and straightforward.


The Keras library was developed - with quick experimentation as its main selling point. Keras neural networks library is Pythonic and supports TensorFlow and Theano. It uses both, convolutional and recurrent networks.

While the TensorFlow interface is complex and can be complicated for beginners, the Keras deep learning framework objectifies the ease-of-use for prototyping, i.e., by building active neural networks compatible with TensorFlow. In short, Keras is easy-to-use and has a minimalist approach.


MXNet is a deep learning framework that runs on Python, R, C++, and Julia. It was developed to be efficient, productive, and flexible. The factor that makes MXNet one of the most popular deep learning frameworks is its functionality of distributed training. In addition to that, it provides nearly linear scaling efficiency.

It lets users write code in Python, C++, R, Julia, and Scala. Meaning you don't have to learn a new language to train your deep-learning models. Instead, you can use any language you've got a firm grasp of.

The Microsoft Cognitive Toolkit/CNTK

Previously known as CNTK, Microsoft Cognitive Toolkit is an open-source deep learning framework that trains DL models. It efficiently performs Convolution Neural Networks and training for speech and text-based data. 

Due to the small building blocks, users can write code in a low-level language to create new complex layers.

Why Did PyTorch Overtake TensorFlow?

PyTorch overtook TensorFlow due to the ease it offers when it comes to debugging a program. Other than that, you can leverage debugging tools like PyCharm debugger, PDB and ipdb. On the other hand, building machine learning models on PyTorch is more intuitive, which is a significant pro for PyTorch vs TensorFlow. Whereas that's not the case with TensorFlow; users need to be well-acquainted with its working. Making it a more complex and less efficient option when TensorFlow and PyTorch are compared. 

Is Learning TensorFlow Difficult?

It'll be a fun and easy process if you know how to code well. But, the answer to this question differs from a learner-to-learner. Some people find it hard to learn, while others find it easy. TensorFlow is also different from other programs because it uses a symbolic programming paradigm method. So, symbolic programming will be easier once you get used to it and grasp the basic knowledge of it.

How Long Does It Take To Learn Pytorch?

If you've written Python code for three to six months and are willing to learn, you're more than ready to go! Plus, if you've known about machine learning for over a year, learning about the PyTorch framework or library is going to be easy for you, and it'll take a maximum of a few months.

Is Tensorflow a Coding?

Building ML models on TensorFlow and PyTorch takes a lot more than just knowing Machine Learning concepts. You need to know how to code, manage data, tune parameters, and parse results for model testing and optimization.

Also, knowing the basics of ML theory will help you build on what you already know and troubleshoot if something goes wrong.


Is PyTorch Written in C++ or Python?

PyTorch's backend is written in C++ due to its basis on Torch, which is written in C.

Is TensorFlow Just for Python?

TensorFlow can be used in many different programming languages, such as Python, JavaScript, C++, and Java. Due to its flexibility, its application can be found in every niche and field.

What Is Tensorflow Used For?

TensorFlow supports data automation, model tracking, and model retraining.

Is PyTorch faster than NumPy?

The answer to “ is PyTorch faster than Numpy ” is a yes. For simple matrix multiplication, PyTorch is almost 15 times faster than Numpy.

Final Note

Now that you’ve read about ML frameworks and “ is PyTorch faster than Numpy ” the deep learning market is to reach USD 18.16 billion by 2023. And, Ziprecruiter states that AI Engineers are expected to make an average of USD 164,769 annually. This indicates that Machine Learning and Deep Learning are significant concepts that are here to stay.

Standing on the foundational concept of software development, you can connect with top software development companies and get effective software developed to aid smooth business operations. To come across the best development companies, head towards Distinguished. On this B2B service-providing platform, you can find the top service providers for your development project. 

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