Artificial intelligence technologies are appearing in more and more areas every day. Deep learning models are used in many systems, from automatic translation systems to medical image analysis, from smart assistants to fraud detection applications. However, creating and training these models is often quite complex. This is where Keras, an open-source tool that simplifies developers' work, comes into play. So, what is Keras?
What is Keras? Its Origin and Purpose
What is Keras? What is the origin and purpose of Keras? Keras was developed in 2015 by Google engineer François Chollet. Keras's goal was to both simplify and accelerate the development of deep learning models. Written in Python, Keras supported various frameworks such as Theano and Microsoft CNTK in its early years. Today, Keras is most commonly used with TensorFlow and has become its default interface since TensorFlow 2.0. Keras offers a user experience free from complex mathematical details. Operations such as creating model architectures, training, and testing the model can be easily accomplished with a few lines of code. This feature makes Keras appealing to both beginners and experienced developers.
How Does Keras Work?
So, how does Keras work? Keras is essentially a high-level API. When designing a deep learning model, a developer simply decides which layers to use, what activation functions to use, and what optimization method to use. A powerful underlying engine like TensorFlow handles all the remaining low-level processing. This separation allows researchers and developers to focus on the model's logic while leaving the mathematical computational details behind Keras. For example, it's possible to create a fully functional image classification network with just a few lines of code. Keras automatically optimizes, trains, and evaluates this model using TensorFlow.
The Role of Keras in the Deep Learning Process
The basic steps in a machine learning or deep learning project are generally the same: data preparation, model definition, training, and evaluation. Keras offers a highly intuitive interface for each of these steps. It works well with libraries like NumPy and Pandas for data-related operations. For model creation, it incorporates fundamental building blocks like layers, activations, and loss functions. The training process can be initiated with a single command, and visualization of the process can be easily integrated with tools like TensorBoard. Furthermore, Keras models can be optimized to run on both CPU and GPU, making them suitable for both small-scale experiments and projects working with big data. For more information, you can read our article [
What is Deep Learning? ](https://plusclouds.com/tr/blogs/deep-learning-nedir-deep-learning-ve-machine-learning-arasinda-ki-farklar-nelerdir).
Keras Usage Areas
Today, Keras is used in a wide range of fields. Its applications range from models that recognize handwritten digits in image processing to medical image analysis systems that detect cancer cells. It produces effective results in natural language processing projects such as translation, sentiment analysis, and automatic summarization. Keras is also frequently used for complex tasks such as financial time series forecasting, autonomous vehicles, and speech recognition.
Academic and Industrial Use
What is Keras? Keras is a widely accepted tool in both academic and industrial projects. Researchers use Keras to rapidly prototype their experiments, while companies test their models in this environment before moving them to production. Because it's compatible with TensorFlow, models are easy to deploy and integrate into real-time applications. A model developed with Keras can be installed on a server with TensorFlow Serving, ported to a mobile device with TensorFlow Lite, or run in a web browser using TensorFlow.js. This flexibility offers developers a wide range of options depending on their application.
A Strong Infrastructure with PlusClouds
 Powerful deep learning libraries like Keras require significant processing power during training and testing. This requires an efficient and scalable infrastructure. PlusClouds offers customized cloud infrastructure solutions for your AI projects. With features such as GPU-supported servers, high-performance data transfer, scalable storage systems, and automatic resource management, PlusClouds provides an ideal environment for Keras users. Whether you're in the R&D phase or planning a transition to production, PlusClouds' infrastructure is with you at every stage of your projects. For more information about PlusClouds' AI solutions, please visit www.plusclouds.com.
Frequently Asked Questions (FAQ)
**What is Keras? Is Keras free?** Yes, Keras is completely open source and free. It is distributed under the MIT license. **Which programming language is used with Keras?** Keras is written in Python and can only be used with Python. **Are Keras and TensorFlow the same thing?** Keras is a high-level API that is part of TensorFlow. It can be used directly with TensorFlow 2.0 via the tensorflow.keras module. **Do Keras models run on mobile devices?** Yes. Keras models can be ported to mobile environments such as Android and iOS using TensorFlow Lite. **Can Keras models run in real-time?** Yes. Models integrated into servers with TensorFlow Serving can work with real-time data as a REST API.
Conclusion
Keras opens a powerful yet accessible gateway to deep learning technologies. Its ease of use, robust infrastructure, and extensive community support not only accelerate the learning process but also enable you to develop production-quality solutions for industrial projects. Proper hardware and cloud infrastructure are crucial for maximizing the effectiveness of this tool. PlusClouds supports this process with specialized solutions for your AI-based projects and provides developers with an end-to-end platform. If you're looking to take a solid step in your AI journey, you can start exploring Keras and taking advantage of the capabilities PlusClouds offers. To browse articles similar to "What is Keras?": [
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