Keras vs. Pytorch: What’s the Difference?

The demand for artificial intelligence (AI) skills is surging, and the global AI market is projecting a 26.6 percent collective annual growth rate (CAGR) from 2025 to 2031 [1], making now a great time to develop your skills in this field. One area of AI worth looking into is deep learning, where you can train complex neural networks to learn from massive amounts of data and make predictions for uses like natural language processing or image recognition. 

Keras and PyTorch are among the most valuable tools you can master for deep learning applications. Although they both have their place within deep learning, they contribute differently, as Keras is a deep learning API, while PyTorch is a deep learning framework. 

Learn more about the various features of Keras versus Pytorch, and discover how you can utilize both to develop powerful deep learning models. 

What is Keras?

Keras is a deep learning API that you can use to train and build deep learning models, first released in 2015 with Keras 1.0. The latest version of Keras, Keras 3.0, provides multi-framework functionality, so you can use it with PyTorch, TensorFlow, or Jax, allowing you to build and transfer your deep learning models between frameworks. This is beneficial because different deep learning frameworks offer different features, and with Keras facilitating the transfer of your model between frameworks, you can capitalize on the strengths of each. Additionally, since Keras is written in Python, it provides a relatively simple environment to help make deep learning more accessible with concise code and a familiar syntax if you have previous experience with Python.

What is Keras used for?

You can use Keras to develop deep learning models, supporting various aspects such as training, building, and debugging. You can utilize it to build models for all kinds of different deep learning applications, such as natural language processing and large language models, computer vision, recommender systems, and predictive analytics. For example, Waymo, a company developing self-driving vehicles, relies on Keras. Deep learning plays a key role in creating self-driving vehicles as it helps them learn to detect objects and perceive the space around them accurately. Other organizations and businesses like Spotify and Netflix use Keras for building deep learning algorithms to understand customer preferences and predict songs you may like listening to or suggest movies to watch.

Key features of Keras

Keras provides features that make deep learning simpler, so you can use it to develop models quickly, making it suitable for experimentation and prototyping. Keras does this by giving you access to prebuilt deep learning models and data sets while also requiring minimal lines of code, all within a simplistic user interface. Additionally, when you do come across errors in your code, Keras provides clear direction to help you solve problems more easily. Another key feature of Keras is its multi-framework approach, so that you can use Keras alongside other valuable deep learning tools like TensorFlow and PyTorch.

What is PyTorch?

PyTorch is an open-source deep learning framework initially released by Meta (known as Facebook at the time) in 2017. In addition to being written in Python, which makes learning PyTorch much more manageable if you have previous experience with the language, PyTorch also integrates with Python tools and libraries like NumPy and SciPy to enhance its capabilities. Overall, PyTorch helps turn deep learning into a more approachable process, providing neural network architectures and tools to simplify several areas of deep learning, including data preprocessing, coding, training, and debugging for more efficient development.

What is PyTorch used for?

PyTorch simplifies the process of developing deep learning models. Its effectiveness in improving the efficiency of building deep learning models makes it an especially valuable tool for research and prototyping, while still providing everything you need to build models at scale. PyTorch’s widespread offering of tools allows you to apply the framework to many different use cases, including generative AI, natural language processing, and computer vision. For example, Axon used PyTorch to develop AI-powered license plate recognition technology to help law enforcement nationwide identify vehicles of interest. 

Key features of PyTorch

PyTorch’s vast ecosystem gives you the power to perform a wide range of tasks, covering each stage from beginning to end as you go along, building and eventually deploying your model. Take a look at the key features of PyTorch that help make this possible:

• Using TorchScript, you can transition your operations to graph mode, enabling you to run your model in C++ environments.

• The TorchServe tool allows you to deploy models at scale while avoiding the need to write customized code for common applications.

• PyTorch has a dedicated community of developers who actively participate in introducing new tools that expand PyTorch’s capabilities.

• Using the Open Neural Network Exchange (ONNX), you can export your deep learning models to other compatible platforms for interoperability with other frameworks.

Keras vs. Pytorch: Key considerations

Keras and PyTorch are two of your best options when it comes to developing deep learning models, but it’s important to remember that you don’t necessarily have to choose between one or the other since Keras is a multi-framework tool that you can use alongside PyTorch to develop well-rounded models. Understanding the various benefits of Keras and PyTorch, as well as the differences between them, can help you optimize your model. Keras offers a more intuitive architecture if you’re looking for the simplest deep learning tool. If you need large data sets for your model, PyTorch is a better fit. 

Learn more about Keras and PyTorch on Coursera

When you’re developing deep learning models, both Keras and PyTorch are worthwhile additions to your skill set, whether you use them together or individually. On Coursera, you can find highly rated courses to help you practice your deep learning skills. Earn an IBM Machine Learning Professional Certificate, where you have the opportunity to code your own projects and work with tools like Keras. You may also consider the Deep Learning Specialization from DeepLearning.AI, which can help you learn to build and train neural networks using Keras, TensorFlow, and more.