A Comprehensive Guide to Buying Keras

Your Guide to buying the best Keras


Keras is a powerful open-source library for deep learning applications. It provides a high-level interface for building neural networks and performing various tasks such as image classification, object detection, and natural language processing. This buying guide is designed to help you understand the different features of Keras and make an informed decision about which version of the library to purchase. We will go over the different versions available, discuss their key features and applications, and provide guidance on factors to consider when selecting the right version for your project.

Key features

  • Requirements: Consider the computing requirements for running Keras, such as the type of system, memory, and CPU/GPU requirements.
  • Compatibility: Make sure that Keras is compatible with the libraries and frameworks you are already using.
  • Support: Look into the community support available and resources such as tutorials, documentation, and forums.
  • Integration: Check what other services and frameworks Keras integrates with, and consider the value of those integrations for your needs.
  • Budget: Consider your budget, and make sure the version of Keras you purchase is the right fit for you.
  • Features: Understand the differences between different versions of Keras and the features they offer.
  • Scalability: Think about how Keras will scale with your application, and how it can handle future growth.
  • Security: Make sure that the version of Keras you purchase has the necessary security and privacy features.

See the most popular Keras on Amazon

Important considerations


  • User-friendly: Keras is an easy-to-use deep learning library, designed to make it simple to quickly prototype and build models. It provides a set of building blocks that can be used to create complex models without having to write a lot of code.
  • Modular: Keras allows users to develop a deep learning model in a modular fashion, allowing users to add and remove layers, adjust parameters, and swap out optimizers with ease.
  • Flexible: Keras is designed to be highly flexible and allows users to tailor a model to their exact needs. Users can also use different backends for training and inference, allowing them to use different hardware like GPUs and TPUs.
  • Scalable: Keras can be easily scaled to larger datasets, allowing users to set up and train models on a variety of different architectures. This makes Keras ideal for large-scale projects, such as training on a large number of images or audio clips.
  • Compatible: Keras is compatible with a number of different deep learning frameworks, like TensorFlow, CNTK, and Theano. This allows users to use Keras to integrate their existing models from other frameworks with ease.


  • Cost: Keras is a free open source library, so the only cost is the time it takes to learn it.
  • Support: While there is a large online community of Keras users, the support is not always reliable.
  • Learning Curve: Because Keras is a relatively new library, there are not as many tutorials and resources available for learning this library as there are for other deep learning frameworks.
  • Non Support for Older Versions: Keras is regularly updated, which means that earlier versions may not be supported by the newest versions.
  • Limited Size of Networks: Keras is limited to training only small to medium sized networks, which may not be suitable for larger scale data.

Best alternatives

  1. TensorFlow - A popular open source machine learning platform widely used by data scientists and developers.
  2. PyTorch - A deep learning framework that focuses on flexibility, allowing for quick changes to be made to the network.
  3. MXNet - A scalable deep learning library designed for both efficiency and flexibility.
  4. Caffe - A deep learning framework designed for speed and accuracy.
  5. CNTK - Microsoft’s open source deep learning framework designed to be easy to use but still offer powerful features.

Related tools, supplies, and accessories

  • Keras - A deep learning library written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
  • GPUs - Graphics processing units that are specialized hardware designed to accelerate the computations necessary for deep learning.
  • Anaconda - A package and environment manager for Python with an extensive list of pre-packaged libraries for deep learning.
  • CUDA Toolkit - A parallel computing platform and programming model developed by NVIDIA for general-purpose computing on GPUs.
  • cuDNN - A Deep Neural Network library developed by NVIDIA for GPU acceleration of deep learning.
  • Python - The programming language used to write Keras code.
  • Jupyter Notebook - An open-source web application that allows you to create and share documents containing live code, equations, visualizations and narrative text.

Common questions

  1. What is Keras?
    Keras is an open-source neural network library written in Python. It is capable of running on top of other popular deep learning libraries such as TensorFlow, Microsoft Cognitive Toolkit (CNTK), and Theano. It allows for fast experimentation through its high-level, user-friendly API.
  2. Which platforms does Keras support?
    Keras is platform-agnostic and can be used on any platform, such as Linux, Windows, Mac, and even mobile platforms such as iOS and Android.
  3. What type of neural networks can be built using Keras?
    Keras is capable of building any type of neural network, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long-short term memory (LSTM) networks, and more.
  4. Is there a cost associated with using Keras?
    No, Keras is open-source and free to use.
  5. What programming language is Keras written in?
    Keras is written in Python.
  6. What libraries does Keras require?
    Keras requires a deep learning library such as TensorFlow, Microsoft Cognitive Toolkit (CNTK), or Theano.
  7. What is the best way to learn Keras?
    The best way to learn Keras is to take a course or read tutorials and articles on the topic. Additionally, there are many online resources and discussion forums available for help and support.


Keras, an open-source neural network library, has an interesting origin story. It was created by François Chollet, a software engineer at Google, who named it after the Greek word for 'horn,' because he thought the name was funny. Chollet wanted to create a more user-friendly deep learning library that was easier to use than other existing libraries. He released the first version of Keras in March 2015. Since then, it has become one of the most popular deep learning libraries in the world, and it is used by thousands of developers and data scientists from all over the globe. Source

Disclaimer: This buying guide was not created by humans, and it is possible that some of it's content is inaccurate or incomplete. We do not guarantee or take any liability for the accuracy of this buying guide. Additionally, the images on this page were generated by AI and may not accurately represent the product that is being discussed. We have tried to convey useful information, but it is our subjective opinion and should not be taken as complete or factual.