Predictions have always been subjectively taken. As a result, we have always taken astrology with a pinch of salt. However, machine learning has changed predictions for entrepreneurs once and for all. Entrepreneurs tap into the power of machine learning to get better insights into the existing loopholes in their systems. The possibility of errored processes decreases with the use of forecasting and predictive analytics.

However, we can’t move past the capabilities of python and machine learning in that area. While there are numerous python libraries available, PyTorch and Keras have gained quite popularity in recent times.

The AI developers are finding a renewed interest in studying the comparison between PyTorch and Keras to explore more about the machine learning capabilities. In the past five years, the interest in these two python libraries has indeed called for PyTorch vs Keras comparison to identify their best use cases.

In this blog, we will explore the comparison between these two in detail.


Facebook released PyTorch back in 2017. PyTorch is an open-source python library in machine learning. The python library is popular for its fast and supple experimentation capabilities. In addition to this, PyTorch finds its application for the development of cross frontend and its scope is constantly evolving with the need for deep learning and machine learning.

PyTorch leverages the math processing unit alongside the graphical processing unit. PyTorch works with minimal resources possible and hence it utilizes the built-in memory to the optimum. Since PyTorch is a neural network, it has benefits over numerous machine learning programs. The python library is basically used for offering suppleness for deep learning. Besides this, it is also used where the computational graphs have to be developed in advance.

Usually, PyTorch works closely with the TensorFlow and has a great speed of training.

What are the Best Benefits of PyTorch?

Below are some of the best benefits of using PyTorch:

  • Numerous Python developers find PyTorch easy to learn and is efficient to code.
  • PyTorch has a rich set of APIs for extending the libraries as per the need.
  • PyTorch features computational graph support during the runtime.
  • PyTorch is known for its speed, flexibility, and offered optimizations.
  • PyTorch also extends its support for the Graphic processing unit beside the computational processing unit.
  • The python developers find it easy to debug the python library with the IDE as well as debugging tools.
  • PyTorch homes numerous powerful APIs, which can be implemented in Linux OS or Windows.
  • The Python library has a vast community of developers as well as researchers, which form an integral part and extend assistance when required.

What are the Drawbacks of PyTorch?

Below are some of the disadvantages of using PyTorch:

  • It has not even been a total of five years since its release. Hence, it is not widely known by the developers’ community.
  • PyTorch does not feature visualization and monitoring tools similar to that of the tensor board, which is a major setback.
  • The python library indeed has a large community of developers. However, the size of the community is relatively smaller than the other frameworks. Hence, when you pick up another python library, you might get more support.

What are the Best Applications of PyTorch?

Let’s explore some of the best applications of PyTorch:

  • PyTorch Finds Application in Computer Vision:
    PyTorch is utilized as a convolution neural network for the development of image classification, generative application, and object detection. Through PyTorch, a python developer can process images as well as videos for the development of a highly accurate as well as precise computer vision model.
  • PyTorch can be used for Natural Language Processing:
    PyTorch can be used for the development of language translators, language modelling, and for the development of chatbots. PyTorch uses LSTM, RNN, etc. In addition to this, it has the necessary architecture for the development of natural language processing models.
  • PyTorch for Reinforcement Learning:
    PyTorch can be used for the development of Robotics for automation. In addition to this, it can also be used for business strategy planning or robot motion control. In addition to this, it utilized Deep Q learning architecture for building a model.

Now that we have learnt about PyTorch, it is equally important to know about Keras to understand PyTorch vs Keras in a better manner.


While there are numerous deep learning frameworks available today, the obvious question is why choose Keras. This section will help us understand PyTorch vs Keras, while differentiating the unique features of Keras.

Keras emphasizes the developer experience. It is an API designed for human beings and not just machines. Keras reduces the number of actions required from users for typical use cases. In addition to this, it also offers clear as well as actionable feedback upon the user error. As a result, it becomes easy to use and learn. Keras developers find more productivity in their tasks while using the python library. In addition to this, it offers great speed, which enables you to win over your competitors with better time to market.

Among the other Python libraries, Keras has over a million individual developers. Besides this, Keras has high adoption across the research community and industry.

What are the Best Benefits of Keras?

Let’s take a look at the benefits offered by Keras.

  • One of the best benefits offered by Keras is its simplicity to use.
  • Keras find it difficult to operate on low-level computations. Hence, it supports backend use.
  • Keras has a wide range of pre-trained models, which helps developers in simplifying their tasks.

What are the Drawbacks of Using Keras?

Some of the disadvantages of using Keras are as mentioned below:

  • Keras features some of the improvable features. The errors thrown by the python library are ineffective. Hence, there is room for improvement.
  • Keras offer you low-level errors numerous times. The key reason for these errors is based on the operation and functionality that is far beyond the capability of Keras.

What are the Top Applications of Keras?

Below are some of the best applications of Keras:

  • Keras for Deep Learning:
    Keras features an easy-to-use and powerful library for the evaluation and development of deep learning applications. The python library has efficient computational capabilities, which enables developers to train and define neural network models with the help of a few lines of code.
  • Keras for Reinforcement Learning:
    Keras can be utilized to enhance cognitive thinking ability with Neural Networks and Deep Learning. It can be used for reinforcement learning by training it through the stimulation of games.
  • Keras for Image Classification:
    Image classification is one of the most practised areas in machine learning. Keras can be used for practical use cases, which requires image classification, gender detection, security surveillance, and more.

PyTorch vs Keras: A Detailed Outlook

While there are many intersecting areas for PyTorch vs Keras, there are numerous points of difference between the two python libraries. In this section, we will pinpoint the key points of difference between PyTorch vs Keras for better understanding their application in custom python development.

Let’s take a look at PyTorch vs Keras:

Accessibility and Debugging:

The functionality and performance of Keras and PyTorch are near-similar. When we talk about speed, both the python frameworks use asynchronous execution for queueing jobs for avoiding time-consuming read and write operations. Both of these python frameworks are well capable of distributed workloads over numerous graphical processing units and resource-optimizing precision computation. However, based on the speed, Kerashas more maturity as compared to PyTorch.

Documentation, Bug-fixes, and Version Management:

Keras has been through multiple transformations in execution, outlook, and intent as compared to PyTorch. Some of the documentation of Keras lack practical solutions for coping up with the popularity of the framework. As compared to PyTorch, Keras take a longer time to respond to the bug reports.

Open Issues:

There are about 31,000 open issues pertaining to Keras, which are around 9,000 for PyTorch. Though it shows the popularity of Keras, it means that the number of issues encountered with PyTorch is significantly lesser. PyTorch maintains a helpful forum for users, while that of Keras defers to the community of Stack Overflow. Despite being released after Keras, PyTorch has better documentation than Keras.

PyTorch vs Keras: The Sum and the Substance

PyTorch vs Keras–there are numerous areas where these python libraries find applications. On the other hand, there are areas, where they differ completely. Hence, when a product development agency has to decide between PyTorch vs Keras, it is crucial to refer to the product requirements and jot down the key points of these python libraries in order to identify the best one as per the requirement so you can even carry out auditing of your code with ease.


If you are unable to do so, we have a panel of experts to help you decide.

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