Deep learning is a subset of Artificial intelligence(AI). This field is growing in the past decades with an increase in the community of developers.
As deep learning is very complex a variety of libraries are made and developed which makes it easy to understand and gives better visualization.
PyTorch and TensorFlow are the two most popular libraries designed to deliver the best visualization and understanding to build neural networks for developers.
Table of Contents
We will discuss PyTorch and TensorFlow in this article.
Because of these two libraries, many millions of developers of machine learning and deep learning is dependent on and rely upon them.
We will differentiate between these two libraries and tell you which one is best for you.
What is PyTorch
Pytorch was first released in 2016 by the Facebook AI research lab and quickly became very popular among developers and researchers.
It is an open-source library written in programming language python and is used by developers and researchers who are willing to build or do tasks in deep learning or machine learning.
It’s API is very well designed and feels more pythonic. This library is developed for easy-to-understand and better visualization.
Features of PyTorch
- The PyTorch community is very much spreading and many developers are contributing to taking PyTorch to a new level that can support areas like computer vision, and reinforcement learning.
- PyTorch is accepted by all the top cloud service platforms and offers without hassle larger scale preparation of GPUs and much more.
- PyTorch offers two really useful features which are dynamic computational graphs and imperative programming. This feature is very useful in neural network models.
- PyTorch also allows you to easily modify the code on a lower level so that it doesn’t feel much more complicated.
What is TensorFlow
TensorFlow is an open-source, end-to-end platform for machine earning. It is developed by the google brain team in 2015. It was written in programming languages like python, C++, and Cuda.
It is also an open-source library same to PyTorch. TensorFlow improved the performance of Google applications like Gmail, Photos, and Google search.
Features of TensorFlow
- TensorFlow provides a comprehensive environment of tools for enterprises, developers, and researchers to push their machine learning model as well as to build some scalable machine learning-powered applications.
- All calculation process is described in the form of graphs. However, no matter what the calculation is, it will be described in the calculation graphs.
- TensorFlow supports Distributed processing which can handle a large amount of data such as Big data also.
- TensorFlow allows the developers to create a data flow graph which is a structure that describes how the data move through a graph or a series of processing nodes.
Key differences between PyTorch and TensorFlow
|Origin||It is developed by Facebook in 2016.||It was developed by Google in 2015.|
|Library||It was developed from the Torch library.||It was deployed on a python library i.e theano.|
|Community||Smaller community as compared to TensorFlow.||A Large community of developers.|
|API Level||Simple API is used.||More complex and high API is used.|
|Speed||Faster and high performance.||Faster and high performance.|
|Popularity||Third most popular.||Second most popular.|
|Features||PyTorch has fewer features as compared to TensorFlow.||TensorFlow offers more features and great choices than PyTorch.|
|Graph Constructing and Debugging||Debugging is easy to conduct.||Difficulty in debugging.|
|Deployment||Complex, and less readable.||Not easy to use.|
|Architecture||It’s easy to understand and learn.||It’s more complex to understand and learn.|
|Visualization||Visualization is poor as compared to TensorFlow.||TensorFlow is best in visualization.|
Similarities of PyTorch and TensorFlow
Both of these libraries are open source and free for all available libraries.
Both of these libraries have trained models.
Both of these libraries offer useful abstraction in project development by reducing the boilerplate codes.
Both of these libraries can also run on the same environment now.
Both of them are very fast and better at visualization.
We have discussed both PyTorch and TensorFlow in this article and also their differences.
Both are good libraries to be used by a deep-learning developer. We can’t define which one is perfect because in PyTorch we get an easy understanding whereas in TensorFlow it’s complex. PyTorch offers less visualization than TensorFlow.
So one can choose any of these libraries as per their requirements. If you are a beginner then you must use the PyTorch library due to its simplicity and If you are a good and intermediate developer then you go with TensorFlow.
What is the difference between PyTorch and TensorFlow?
PyTorch offers dynamic computational graphs and imperative programming which means you can check for bugs before writing the full code.
Whereas TensorFlow offers flexible and good support for mobile and embedded deployments.
Is TensorFlow or PyTorch easier to use?
PyTorch is easy to understand as compared to TensorFlow because of its easy syntax it gains an advantage over other deep learning libraries.
Also, PyTorch is much easier to work with in case of debugging. Simple, Flexible, computational graphs and imperative programming are some of the reasons that make PyTorch an easy-to-understand library.
Is PyTorch more efficient than TensorFlow?
Though these both are on the same level of performance wise PyTorch gets an advantage because of its pythonic approach.
PyTorch is an easy-to-use and understandable library whereas TensorFlow is a complicated library to use for a beginner.
Is PyTorch faster than TensorFlow?
No Doubt PyTorch is faster than Tensorflow.
PyTorch is easier to code which makes it faster than Tensorflow which is complicated on the other hand.
How efficient is PyTorch compared to TensorFlow?
On implementations, PyTorch and TensorFlow show equal accuracy, and Performance-wise, both frameworks are on the same level.
But PyTorch is more efficient due to its simple API and the ease of debugging.
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