Introduction to Keras

Features of Keras

• It runs fine on both CPU and GPU.
• It supports both convolutional and recurrent networks and the combination of two and almost all neural network models.
• It enables easy and fast prototyping.
• It is modular in nature and supports arbitrary network architectures, making it expressive, flexible, and appropriate for building deep learning models and innovative research works.

Keras relies on a specialized and well-optimized tensor library which serves as the backend engine of Keras. At this time, Keras has three backend implementations available:

• TensorFlow: It’s an open-source symbolic tensor manipulation framework developed by Google.
• Theano: It’s an open-source symbolic tensor manipulation framework developed by LISA Lab at Université de Montréal.
• CNTK: It’s an open-source toolkit for deep learning developed by Microsoft.

Why do we need Keras?

• Keras is an API that was created with the idea of "easy to learn for people" and is designed for human beings and not machines; it was made to be simple. It offers consistent & simple APIs, and follows best practices for reducing cognitive load.
• Prototyping time is significantly less in Keras, which means that your ideas can be implemented and deployed in a shorter amount of time. It also provides a variety of deployment options.
• Keras runs on the top of TensorFlow and is relatively faster. It is also integrated deeply with TensorFlow so that customized workflows can be created with ease. Various languages with a high level of abstraction and inbuilt features are slow, and building custom features in them can be challenging, But it can be done quickly in Keras.
• Keras has a vast and highly developed research community. The guidance available is far more comprehensive than other available 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. 

How to Build a model in Keras 

1. Define a network: We start by defining the different layers in our model and the connections between them. Keras has two main models: Sequential and Functional models that map the input to our targets. We select which type of model we want and then define the data flow between them.
2. Compile a network: Compile code to convert it in a form which is understandable for the machine. In Keras, the model.compile() method performs this function. To compile the model, we define the loss function to calculate the losses in our model, the optimizer to reduce the loss, and other metrics used to find our model's accuracy.
3. Fit the network: After compiling, we fit our model to our data. Iterate your training data by calling the fit() method of your model.
4. Evaluate the network: After fitting is done, we need to evaluate the error in our model.
5. Make Predictions: We use "model.predict()" to make predictions on new data using our model.

Applications of Keras

• Deep models require a lot of computation power to run; Keras is used for 
• distributed training of deep learning models. We can split our Deep Learning model into different parts and train it on various systems all across the globe. It makes training very fast and also saves the computational power of our system.
• Keras is used by many companies such as Uber, Yelp, Netflix, etc.
• Keras is also extensively used to create and deploy working models, which are fast in a short amount of time.
• Keras is used by around 200,000 users, including commercial use, academic research, and engineering jobs at startups and large companies like Google, Netflix, Uber, Microsoft, Square, and many startups working on a wide range of machine learning problems.

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Frequently Asked Questions

1. How to use pre-trained models in Keras?
   There are multiple models available in Keras.applications, or the models available on TensorFlow Hub can be accessed. TensorFlow Hub is well-integrated with Keras.
    
2. How to use Keras with datasets that don't fit in memory?
   It will help if you use the tf.data API to create tf.data.Dataset objects. It's an abstraction over a data pipeline that can pull data from local disk, from a distributed file system, from GCS, etc., and can efficiently apply various data transformations as well.
    
3. What is Perceptron in a Neural Network?
   In the Neural Network, Perceptron is a unit that does certain mathematical computations to detect features in the input data. It’s a function that maps its input x, which is multiplied by the learned weight coefficient and generates an output value f(x).

Key Takeaways

This is the basic overview of Keras. In this blog, we understood the basic working of Keras along with its features, applications, and steps to build a model in Keras. I hope this blog gave you an introductory understanding of Keras.

However, the theory is one aspect of learning, and its practical application is another. So get up and get going. Please utilize this knowledge to learn more and try practical applications of Keras. I hope this article was helpful.