Nowadays, AI and machine learning continue to grow and become popular. Over time, developers realize they need another branch which is deeper and more specific to solve more difficult and highly complex problems. Deep learning has emerged like that. It’s based on how human brain works, uses neural networks as the core of methodologies. However, to implement neural networks and make operators break down the problem into smaller parts and solve each of them, we will need various libraries like Theano, TensorFlow, Caffe, Mxnet, Keras etc.
Therefore today, let’s take a look at Keras, one of the best deep learning frameworks. We will try to introduce it and explain some benefits when using it.
Keras is a high-level API designed for Python to implement neural networks easier. It’s developed by Google.
Keras can run on top of the libraries and frameworks such as TensorFlow, Theano, PlaidML, MXNet, CNTK. They are all very powerful libraries but difficult to understand for creating neural networks. Keras, on the other hand, is very beginner-friendly because of its minimal structure that provides a clean and easy way to create deep learning models based on TensorFlow or Theano.
Keras has been adopted by TensorFlow as its official high-level API. When embedded in TensorFlow, it provides inbuilt modules for all neural network computations and therefore can perform deep learning very fast. TensorFlow is very flexible and the primary benefit is distributed computing. You can be flexible and can control over your application, implement your idea in a short time, using Keras, while computation involving tensors, computation graphs, sessions, etc can be custom made using the Tensorflow Core API.
Keras is very powerful and dynamic. But what make it really stand out and become a popular choice? It comes from Keras’ features set and benefits.
- Keras is very simple to learn and understand. Moreover, it’s a consistent and extensible API.
- Minimal structure makes it easy to achieve the result.
- It supports many platforms, backends, neural networks models.
- Keras neural networks are written in Python.
- It runs on both CPU and GPU smoothly.
- Large community support with many users and documentation, ready to help than other deep learning frameworks.
- Keras is the choice of many big names like Netflix, Uber, Square, Yelp, etc. for their products in the public domain.
A simple yet flexible tool for innovative research, a tool which has gained recognition of many developers, we can now understand why it’s used widely. Developers often use it to create deep models for smartphones, for distributed training of deep learning models. Also, they can use it to create and deploy working model in a short time.
In this article, we hope that we deliver to you the definition of Keras and why you should use it. It comes from the versatility and simplicity of Keras. It runs well with Python and TensorFlow, which is the selling point if you want to find a tool for AI, machine learning and especially deep learning.
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We support many programming languages, libraries, frameworks or API like Keras. You are free to install any of them on our remote servers, add your license (if required) and start your project.
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Source: simplilearn.com, tutorialspoint.com