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PyTorch vs TensorFlow: What is Best for Deep Learning?

Python vs TensorFlow

Do you know that nowadays, modern technologies are in the race to solve the most complex human problems?

You must be very well aware as you are reading this article. Out of all these modern technologies, deep learning has a different fan base and following. Deep learning tries to solve all these problems by simulating human brain functioning using neural networks. However, the growing popularity also attracts comparison with competing libraries, like "PyTorch vs. TensorFlow" which is the better of the two.

In this age of digitization of data, deep learning is growing in its scope incessantly, from self-driving cars to solving complex puzzles of the human brain and body, from the entertainment industry to virtual assistants, and from chatbots to banking services. Therefore, machine learning technologies like deep learning have started to become omnipresent.

Hence tech giants like Facebook, Google, Tesla, Uber, and many more are working to make the most out of deep learning. TensorFlow and PyTorch are two major Python deep learning frameworks that came out of this hustle.

BASICS: TensorFlow and PyTorch

What is TensorFlow?

TensorFlow is a free, and open-source library based on Python. It is mainly used for developing deep learning applications especially those related to machine learning (ML) and artificial intelligence (AI).

What is PyTorch?

PyTorch is also an open-source and free framework based on the Torch library. It offers greater flexibility and increased speed for deep neural network implementation.

  • Parent- Facebook
  • GitHub- PyTorch GitHub
  • Platforms- Intel x86 (32 and 64 bit)

Applications of TensorFlow & PyTorch


  1. Image recognition: It follows a standard procedure that includes- sorting out pixels of the image, getting features of the pixels, training the image, training the model, and testing the model against the inputs.

  2. Audio-Video recognition: It is done through automated audio and video detection framework available within TensorFlow. Also, it finds its application in IoT, voice search, sentiment analysis, flaw detection, etc.

  3. Time Series analysis: To get insightful statistics out of time series data for purposes like- analyzing customer activity, behavioral targeting, etc. Most of these functions are used by big players like- Google, Facebook, and Netflix, to analyze the end user’s activity.

  4. Text recognition & detection: TensorFlow can also be used for language detection, translation, handwriting pattern recognition, etc. The most common application of them is at banks and insurance companies in fraud detection.


  1. Natural language processing- It can be used in developing ML-based models, language translators, chatbots, etc.

  2. Computer vision: It can be used in object identification, object detection, image classification, image processing, etc.

  3. PyTorch reinforcement learning: PyTorch can be used to train deep Q learning architecture to develop models for automation, robotics, etc.

  4. Image classification: This includes convolutional neural networks (CNN) that simulate the functioning of the human brain.

  5. Text recognition: For training an AI-based text recognition model, recurrent neural network (RNN) and PyTorch are used. Other similar applications based on this include- handwriting detection, pattern recognition, etc.

  6. Time Series forecasting: A package made on top of PyTorch known as PyTorch Forecasting is used for forecasting time series with neural network architectures for real-world cases.

Difference between PyTorch and TensorFlow

Now after understanding the applications and use cases of PyTorch and TensorFlow in deep learning, let’s try to understand which is the best deep learning framework-

1. Graph construction

Computational graph construction takes a different track in both. On one hand, it is static for TensorFlow, and on the other dynamic for PyTorch.

Applications of TensorFlow & PyTorch

RESULT: PyTorch is a clear winner when it comes to computational graph construction.

2. Serialization

Serialization is the process in which the entire graph can be saved as a protocol buffer. Serialization includes API work, cross-language support, and functioning among others to work more efficiently.

Difference between PyTorch and TensorFlow

RESULT: TensorFlow wins the serialization race due to the wide range of services and functionalities it provides.

3. Debugging

Debugging is essential to finding what exactly is breaking the code. And, like multiple other Python tools, TensorFlow also provides different classes and packages to make this simpler. Let’s analyze PyTorch and TensorFlow from this aspect

PyTorch vs TensorFlow

RESULT: PyTorch is a clear winner here as well. This is because you don't have to put any extra effort into debugging.

PyTorch vs TensorFlow: A head-to-head comparison

Comparison for a more clear picture of which way to go.

PyTorch vs TensorFlow

PyTorch vs TensorFlow: Which way to go?

While the above discussion and comparison might have put you in a dilemma as to which framework to choose, to make your life easier, let's have a look at these 4 crucial keys:

PyTorch vs TensorFlow

  • Key 1- Deployment & scalability If your project has a large scope and needs large-scale deployment then your choice should be TensorFlow. On the other hand, if it is just prototyping for a research project at a smaller scale or anything alike then PyTorch should be the option.

  • Key 2- Hobbyist vs expert If you’re a beginner to deep learning, doing a project as a hobbyist, college project, or anything alike then PyTorch should be your obvious choice. However, if the game is serious, and involves cross platforms then TensorFlow comes in very handy.

  • Key 3- Resource optimization & utilization If you’re looking for better utilization and optimization of resources like GPU then nothing can beat PyTorch. However, when it comes to TensorFlow it uses all of the GPU’s capacity available at that moment. Thus, slightly sluggish in functioning.

  • Key 4- Personal interest This is the most crucial key that can be a game changer. Look into your project needs and tech stack and choose what best suits your requirements by keeping in mind all the scenarios we explained above.

PyTorch vs TensorFlow: Closing notes

I hope the picture is much clearer now. To wrap up, no framework can be tagged as a complete solution for your deep learning needs. That goes the same for PyTorch and TensorFlow. It is the utility, functionality, project scope, interest, and expertise that should be looked into before reaching a final decision. Hence, just compare the scope, your requirements, and your interest before making a solid decision. That’s all from the PyTorch vs TensorFlow debate.

Additionally, don’t forget to tell us which way you did choose to go and why. We would like to hear your thoughts. Plus, feel free to suggest something that we missed. If you're an expert Python dev looking for some awesome new gig try remote work through Turing.


  • Content Writer -

    Abhishek Jaiswal

    Abhishek is a Geek by day and Batman by night. He loves to talk about Data and his passion encircles around Trekking, Hitch Hiking, Gardening, and analyzing Ancient Indian Texts. His geeky stuff got highlighted at Microsoft, Code Project, C-sharp Corner, etc.

Frequently Asked Questions

It’s relative. However, companies like Disney, DataRock, etc. are now gradually moving towards PyTorch.

People are mainly moving towards PyTorch due to relative ease of use and time & complexity mechanism.

PyTorch is the most preferred framework for reinforcement learning. We can use this for almost every aspect of deep learning, machine learning, and data science.

View more FAQs


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