Deep learning is a branch of artificial intelligence concerned with solving highly complex problems by emulating the working of the human brain. In deep learning, we use neural networks which use multiple operators placed in nodes to help break down the problem into smaller parts, which are each solved individually. But neural networks can be really hard to implement. This problem is taken care of by Keras, a deep learning framework.

In this article titled ‘What is Keras? The best introductory guide to Keras’, we will introduce you to Keras and explain why it has gained popularity with developers. The topics covered in this article are :

- What is Keras?
- Why do we need Keras?
- How to build a model in Keras?
- Applications of Keras

## What Is Keras?

Keras is a high-level, deep learning API developed by Google for implementing neural networks. It is written in Python and is used to make the implementation of neural networks easy. It also supports multiple backend neural network computation.

Keras is relatively easy to learn and work with because it provides a python frontend with a high level of abstraction while having the option of multiple back-ends for computation purposes. This makes Keras slower than other deep learning frameworks, but extremely beginner-friendly.

Keras allows you to switch between different back ends. The frameworks supported by Keras are:

- Tensorflow
- Theano
- PlaidML
- MXNet
- CNTK (Microsoft Cognitive Toolkit )

Out of these five frameworks, TensorFlow has adopted Keras as its official high-level API. Keras is embedded in TensorFlow and can be used to perform deep learning fast as it provides inbuilt modules for all neural network computations. At the same time, computation involving tensors, computation graphs, sessions, etc can be custom made using the Tensorflow Core API, which gives you total flexibility and control over your application and lets you implement your ideas in a relatively short time.

Figure 2: Keras Backend

## Why Do We Need Keras?

- Keras is an API that was made to be easy to learn for people. Keras was made to be simple. It offers consistent & simple APIs, reduces the actions required to implement common code, and explains user error clearly.
- Prototyping time in Keras is less. This means that your ideas can be implemented and deployed in a shorter time. Keras also provides a variety of deployment options depending on user needs.
- Languages with a high level of abstraction and inbuilt features are slow and building custom features in then can be hard. But Keras runs on top of TensorFlow and is relatively fast. Keras is also deeply integrated with TensorFlow, so you can create customized workflows with ease.
- The research community for Keras is vast and highly developed. The documentation and help available are far more extensive than other deep learning frameworks.
- Keras is used commercially by many companies like Netflix, Uber, Square, Yelp, etc which have deployed products in the public domain which are built using Keras.

Apart from this, Keras has features such as :

- It runs smoothly on both CPU and GPU.
- It supports almost all neural network models.
- It is modular in nature, which makes it expressive, flexible, and apt for innovative research.

## How to Build a Model in Keras?

The below diagram shows the basic steps involved in building a model in Keras:

Figure 3: Building a model

- Define a network: In this step, you define the different layers in our model and the connections between them. Keras has two main types of models: Sequential and Functional models. You choose which type of model you want and then define the dataflow between them.
- Compile a network: To compile code means to convert it in a form suitable for the machine to understand. In Keras, the model.compile() method performs this function. To compile the model, we define the loss function which calculates the losses in our model, the optimizer which reduces the loss, and the metrics which is used to find the accuracy of our model.
- Fit the network: Using this, we fit our model to our data after compiling. This is used to train the model on our data.
- Evaluate the network: After fitting our model, we need to evaluate the error in our model.
- Make Predictions: We use model.predict() to make predictions using our model on new data.

## Applications of Keras

- Keras is used for creating deep models which can be productized on smartphones.
- Keras is also used for distributed training of deep learning models.
- Keras is used by companies such as Netflix, Yelp, Uber, etc.
- Keras is also extensively used in deep learning competitions to create and deploy working models, which are fast in a short amount of time.

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## Conclusion

In this article titled ‘What is Keras? The best introductory guide to Keras’, we first answered the question, ‘What is Keras?’. We then looked at why Keras is so popular and why you should use Keras followed by the basic steps involved in making a model in Keras. We then saw a few uses of Keras.

We hope this article answered the question which was burning in the back of your mind: What is Keras? Do you have any doubts or questions for us? Mention them in this article's comments section, and we'll have our experts answer them for you at the earliest!