Why Google JAX is better preferred.

Oluwafemi Smith
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Feb 06, 2023

The vision of Google has remained clear from the get-go: to ensure that they provide convenient and accessible solutions for all cadres and industries while accessing information at any point.

In line with this vision, several software tools have been created. While the likes of a search engine, email service, maps, Google Lens, Google Business, and a few others are commonly identified by most people, a few tools play a dominant role in machine learning that you probably never heard about.

As said in Latin, "e Pluribus Unum," which means "out of many, one," today’s post will center on one of the great tools created by Google called Google JAX. At the end of this read, you will understand what it means as a tool, what it does, and how it stands out from others in machine learning.

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What is Google Java?

Google JAX is a numerical computation library for machine learning, developed by Google. It is built on top of the popular NumPy library and provides additional functionality, such as support for automatic differentiation and GPU acceleration.

JAX allows developers to write code that is both simple and efficient, and it is compatible with a wide range of machine learning frameworks, such as TensorFlow and PyTorch.

Why did Google make JAX?

Google made JAX to provide a more efficient and flexible way to perform numerical computations for machine learning. JAX is built on top of the popular NumPy library, which provides powerful array manipulation and linear algebra capability, but it also provides additional functionality, such as support for automatic differentiation and GPU acceleration.

JAX aims to make it easier for developers to write code that is both simple and efficient, and it allows for the use of a wide range of machine learning frameworks, such as TensorFlow and PyTorch. The library allows for fast and easy experimentation with different approaches and architectures, making it easier to develop and optimize machine learning models.

Additionally, JAX is designed to be a more expressive and composable numerical computation library; it allows for a more natural and intuitive way to express computations, making it more suitable for research and experimentation in machine learning and other fields.

What makes Google JAX stand out compared to others?

Google JAX stands out from other numerical computation libraries in several ways:

Automatic differentiation: JAX allows for automatic differentiation, which means it can automatically compute gradients of functions concerning their inputs. This makes it easier for developers to implement and optimize machine learning models.
GPU acceleration: JAX provides built-in support for GPU acceleration, allowing for faster training and inference of machine learning models.
Compatible with multiple frameworks: JAX is compatible with a wide range of machine learning frameworks, such as TensorFlow and PyTorch, making it easier for developers to use it in their existing projects.
JAX is built on top of the popular NumPy library, which provides a powerful array manipulation and linear algebra capability. JAX extends NumPy's functionality by adding support for automatic differentiation and GPU acceleration, which makes it more efficient and flexible.
JAX is designed to be a more expressive and composable numerical computation library, it allows for a more natural and intuitive way to express computations, making it more suitable for research and experimentation in machine learning and other fields.

JAX is an open-source library actively maintained by Google. It has a growing community of contributors and users, which means it's a more stable and reliable library to use.

What is the major drawback of Google JAX?

One major drawback of Google JAX is that it is relatively new, and as such, it may have less community support and fewer pre-built modules or libraries available compared to other, more established numerical computation libraries such as TensorFlow or PyTorch.

Google JAX can be challenging to install and configure on certain platforms, especially for developers who are not familiar with the library. It also has fewer features, and it may not be as well-documented or supported as other libraries.

Conclusion.

While Google JAX is mostly focused on machine learning, it may not be suitable for other types of numerical computations that are not related to machine learning.

Nevertheless, these drawbacks may change over time as JAX is actively developed and improved, and more people get to use it.