What Is Keras? Your 2024 Guide

Keras is a platform that simplifies the complexities associated with deep neural networks. Based on principles of user-friendliness, compatibility with Python, and an ability to use across various devices and platforms, Keras excels in faster creation of models and robust support for deployment and adoption. It was created by Francois Chollet, an engineer with Google, and designed for speed and flexibility. 

Released in 2015, Keras has quickly grown in popularity and accounts for nearly 20 percent of the market share, with more than 11,800 users [1]. Its primary uses are for machine learning, artificial intelligence, and big data applications across various industries. Its scalability, ease of use, and straightforward implementation make it one of the leading deep learning frameworks today.

What is Keras used for?

Keras is a framework used for research and development within deep learning. You can also use it to create guides for developers and documentation throughout a project.

Keras simplifies the complexity of creating deep neural networks, providing a user-friendly application programming interface (API) that solves many challenges within deep learning. This productive, high-level API enables users to experiment more freely and overcome many of the challenges within machine learning and deep learning. 

This productive, high-level API enables users to experiment more freely. You can deploy Keras using various platforms, including iOS and Android. You can deploy this platform-agnostic framework using Node.js, Python runtime, and others, which gives it excellent flexibility. In turn, this allows developers to use it across various types of applications.

As the Keras website notes, Keras is "designed for human beings, not machines" [2]. 

Types of Keras models 

You have three ways to create deep learning models when working in Keras. These data structures contain layers forming the units on which you can build deep learning models. Types of Keras model APIs include the following:

• Sequential: You use a single input and output to build the layers of Keras models in a linear stack for simple model development.

• Functional: While you would use sequential models for straightforward tasks, functional Keras models allow you to build more complex models. It supports more than one input and output at a time and offers more flexibility, making it the standard for many users. 

• Subclassing: In some instances, you may have use cases that don't meet sequential or functional Keras modeling standards. In this instance, you can use model training APIs to customize implementation. 

Keras alternatives

Keras is a user-friendly framework with several alternatives available. Three popular options include TensorFlow 2.0, PyTorch, and MXNet. Let's examine each in more detail before reviewing additional details about Keras. 

TensorFlow

Google released this open-source end-to-end framework in 2019, with features like visualization tools, feature columns for simpler data handling, and parallel training, accelerating the time it takes to train models. It's free and offers straightforward debugging with the TensorBoard feature. TensorFlow 2.0 also offers excellent scalability and compatibility with Keras.

Benefits of using TensorFlow 2.0 include:

• Use with Python or JavaScript

• Runs on various platforms, including local or cloud-based

• Allows for high-level work using Keras' library

• Offers abstraction, allowing developers to focus on the logic of the application

• Eager execution mode for evaluating each graph operation individually

PyTorch

Meta developed PyTorch and released the open-source framework in 2016. Users often praise this deep learning framework for its simplicity and support to developers creating complex applications, including those for natural language processing. It features automatic differentiation, integration with Python for an easy-to-use interface and added community support, TorchScript for running models in various environments, and Tensor computation for added speed.

Benefits of using PyTorch include:

• Python-based structure for easier coding

• Straightforward debugging

• Supported on various platforms

• PyTorch library is sought after for deep learning research

• Dynamic computational graphs

• Enables developers to research and build prototypes quickly

MXNet

Apache released MXNet in 2015. This open-source framework supports creating and training models in various languages like Python, Perl, Java, and Julia. Users appreciate its reproducibility, resource utilization, and ability to blend imperative and symbolic programming for fast training and effective creation of neural networks with uses such as natural language processing and image classification.

Benefits of MXNet include:

• Excellent scalability across multiple BPUs and hosts

• Robust ecosystem with support

• Supports various languages, including C++, R, Scala, and more

• Hybrid programming for easier training and deployment

Who uses Keras?

Developers, researchers, and and professionals across various industries, including information technology, education, financial services, and health care use Keras. Some of the world’s leading institutions use Keras, including global organizations and major companies like:

• CERN, the European Council for Nuclear Research

• NASA

• National Institutes of Health

• Google

• Nvidia

• Pepsi

• Infosys

• Panasonic

• eBay

• S&P Global

• Apple

• Uber

• Netflix

If you’re considering working within the field of machine learning, deep learning, or artificial intelligence (AI), you may need to develop your skills and proficiency in working with Keras. A few examples of jobs in which you may use Keras include the following:

Machine learning research scientist

Average annual base salary: $162,625 [3]

Job outlook (2022 through 2032): 23 percent [4]

In this job, you’ll primarily focus on developing AI algorithms and machine learning models. You'll work with data and collaborate with other specialists, such as data engineers and scientists. To work in this field, you typically need a bachelor's degree or higher in data or computer science or another related topic. You may work as a machine learning research scientist in many industries, including the federal government, computer systems design, scientific services, hospitals, educational institutions, or software companies, to name a few.

AI developer

Average annual base salary: $115,711 [5]

Job outlook (2022 through 2032): 25 percent [6]

As an AI developer, you'll create programs with AI functionality, incorporating algorithms into various projects and using deep learning or machine learning as needed. Common tasks include designing and developing AI systems, explaining systems to company leaders, building data architecture, and using deep learning platforms to help answer companies' challenges. Employers typically look for AI developers with a bachelor's or master's degree.

Deep learning engineer

Average annual base salary: $139,399 [7]

Job outlook (2022 through 2032): 23 percent [4]

Although this position is somewhat similar to that of a machine learning engineer, as a deep learning engineer, you'll primarily focus on the beginning stages of data engineering projects. You'll work within the modeling phase to define data requirements, train deep learning models, and deploy code. Bachelor’s degrees are common requirements for entering the field. 

What are the pros and cons of using Keras?

Like any other deep learning framework, Keras has its own unique set of benefits and drawbacks. We examine them briefly below.

Advantages of using Keras

• User-friendly: With its simple API and pre-trained models, Keras is simple to learn and start using

• Flexible: Deployable across various platforms and devices

• Speed: Faster, more intuitive, streamlined research, prototyping, and deployment; Keras also offers fast debugging

• Customization: Simple to customize and share models and their components

• Excellent library: The Keras library features natural, frictionless abstractions

• Ample community support: This open-source framework boasts a sizable community and excellent support

Disadvantages of using Keras

• Limited features: Lacks as many available online projects as alternatives like TensorFlow, and it has yet to provide support for creating dynamic charts.

• Tricky debugging: Although Keras has integrated debugging, it can also pose challenges with tricky errors

• Ineffective library errors: Users often report inefficient library error messages

Take the next steps with Coursera.

Online learning provides flexibility and the option to tailor your education according to your goals and interests. Become more familiar with Keras as you learn about neural networks in the IBM Introduction to Deep Learning and Neural Networks with Keras course. Explore deep learning with the Deep Learning Specialization from DeepLearning.AI to learn how to design, train, and implement deep neural networks. Or prepare for a career in AI with IBM’s AI Engineering Professional Certificate. You’ll find these excellent options and more on the Coursera platform.