Pytorch vs Tensorflow – What’s the Difference?

The main difference between Pytorch vs Tensorflow (as of now, both of these libraries are still evolving) is that more research-oriented developers use the Pytorch library. On the other hand, developers who want to automate things faster and make artificial intelligence related products prefer the Tensorflow library.

In this article, we will explore the difference between Pytorch vs Tensorflow in great detail. But, before we do that, let’s go through the list of topics listed under the table of contents (TOC) that we will cover in this article.

Table of contents (TOC)

• Pytorch vs Tensorflow
• What is Pytorch?
• Advantages of Pytorch
• What is Tensorflow?
• Advantages of Tensorflow
• Pytorch vs Tensorflow - Key Differences
• Conclusion

Pytorch vs Tensorflow 

For a better understanding, let’s go through Pytorch vs Tensorflow in a tabular format:

You can also explore these articles related to Deep Learning:

What is Pytorch? 

Pytorch definition: Pytorch is a deep learning library created by Facebook that works on dynamic graph concepts and uses simple API, which saves the entire weight of the model. 

You can also explore: Pytorch Online Courses & Certifications

The Pytorch library is known for being more prevalent in findings or research than in production. PyTorch rapidly gained popularity among Python developers, making it necessary for the Tensorflow team to incorporate several of PyTorch’s most essential features into TensorFlow 2.0.

PyTorch is also gaining popularity for its ease of use, simplicity, dynamic computational graph, and efficient memory usage.

You must explore: What is Machine Learning?

Advantages of Pytorch

Now that we know what Pytorch is. Let’s explore the advantages of using the Pytorch library:

• Despite being a newer framework, Pytorch has a small but strong community.
• Pytorch is Pythonic, i.e., a person who has knowledge of Python will be able to use the Pytorch library easily.
• Because PyTorch is tightly integrated with Python, it is compatible with a wide range of Python debugging tools.
• PyTorch, like the Python programming language, is thought to be easier to learn than other deep learning frameworks.

You can also explore: Free Pytorch Courses Online

You must explore: Free Python Courses Online

What is Tensorflow?

Tensorflow definition: Tensorflow is a deep learning library created by google that works on the static graph, which can also be saved as a protocol buffer. 

You can aslo explore: Free TensorFlow Courses Online

TensorFlow is more deployment-friendly and, as a result, is commonly used by startups and various other companies to automate processes and create new systems. This library has a reasonably large and active user base, as well as a wide range of authorized and 3rd platforms and tools for deploying, training, and serving models.

TensorFlow’s popularity declined after PyTorch was released in 2016. However, in late 2019, Google launched TensorFlow 2.0, with a significant update that streamlined and improved the library.

Advantages of Tensorflow

Now that we know what Tensorflow is. Let’s explore the advantages of using the Tensorflow library:

• Tensorflow is an open-source platform that anyone interested in working with can use for free.
• Google backs it; hence it is frequently updated and can showcase exceptional performance.
• Tensorflow includes Tensorboard, allowing easy node debugging and reducing the operating costs of visiting the entire code.
• Tensorflow has more data visualization power than any other available library, thus making working with neural networks easy.
• It complies with several programming languages, such as C++, Python, and JavaScript, making it simple for individuals to work in a comfortable environment.
• Tensorflow library deployment is not restricted to any particular device. This property allows Tensorflow to work as effectively on a cellular device, such as android, as it does on some other complex systems.

You can also explore: Free TensorFlow Courses Online

You cab also explore: Free JavaScript Courses Online

Pytorch vs Tensorflow - Key Differences

Here are the key differences between Pytorch vs Tensorflow:

• Tensorflow has more features in comparison to Pytorch.
• Pytorch was developed by Facebook, whereas Google developed Tensorflow.
• Pytorch works on the dynamic graph concept, but Tensorflow works on the static graph concept.
• Tensorflow provides more development support, whereas Pytorch provides less development support and is often used by developers for research than development.

Conclusion

In this article, we have discussed Pytorch vs Tensorflow in great detail, along with topics such as what Pytorch and Tensorflow libraries are, what are advantages of Pytorch and Tensorflow, etc. If you have any queries related to the topic, please feel free to send your queries to us in the form of comments. We will be happy to help!