The concept of Machine Learning emerged in 1952. Arthur Samuel was the first one to come up with the phrase “Machine Learning”. Since then the field of Artificial Intelligence and its subset of Machine Learning has only evolved.
Further, we now even have a subset of Machine Learning known as “Deep Learning”.
The concept of Deep Learning is becoming popular in various fields such as web and mobile app development, data analysis, image visualization, robotics, AI, image recognition, etc.
And thus, there are multiple libraries for Deep Learning like Keras, TensorFlow, Theano, etc.
In this article, we will be discussing one of these libraries, Keras, including its use cases, and its benefits over other libraries in detail.
Keras was developed by Google engineer François Chollet as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System).
Keras is a high-level Python library designed to make Deep Learning fast and efficient. With Keras, any AI enthusiast can learn and train Deep Learning models and programs.
Keras can execute on both CPU and GPU and has cross-platform compatibility. It comes with features that let you develop Deep Learning codes with fewer lines of code.
Features of Keras
Keras has so many features that contribute to its popularity. We are only going to highlight some of the prominent ones:
Since Keras is modular in nature, it is expressive and apt for innovative research. Further, it allows you to save the model you are working on so you can work on it in the future.
Keras provides support for all the models of a neutral network. It can integrate with TensorFlow and allows you to develop workflows in which you can customize any piece of functionality.
Keras features large and useful ready-to-use datasets so you can minimize your code to a great extent. These datasets can also be used for debugging a model.
#4. Evaluation & Prediction
It features two different methods (evaluate and predict) for prediction and evaluation of the data. First, we test the data and then evaluate the result to eventually evaluate our models.
#5. Obtain Output in the Intermediate
Keras allows you to obtain the output in the intermediate of a layer. To do this, you have to create a new layer that will obtain the output. Also, you can build a Keras function to obtain the output of a specific layer using a certain input.
#6. Pre-Trained Models
There are numerous pre-trained models in Keras that can be imported from Keras. application. These models are used for feature extraction, fine-tuning, and prediction.
Encoding is another useful feature of Keras. With the one_hot() function you can encode integers in one step or tokenize your data. Also, you can filter out the white spaces, punctuation, and convert the text to lowercase with the same function.
There are numerous layers and parameters in Keras and each layer has several methods in it. These layers are used to construct, train, configure the data.
Since Keras is a Python library and uses the common concepts of Python, it provides a user-friendly environment. Even if you know only the basics of Python, you can still implement Keras.
Keras has several functions for the preprocessing of data. Take the example of the ImageDataGenerator method, it can be used to resize the image, change its degree, flip the image, change the height and width of the image, etc.
There are various reasons for choosing Keras over other similar libraries:
Reason 1. Open-Source
Keras is not only easy-to-use but also an open-source API for developing complex Deep Learning models. We can expect it to get even better with time to time contributions from the community.
Reason 2. Simple APIs
Keras follows best practices to reduce cognitive load and offers consistent and simple APIs to minimize the number of user actions to common use cases.
Reason 3. Clarity
One of the benefits of Keras is it offers clear and actionable feedback upon user error. This results in quick fixes and fast implementation.
Reason 4. Numerical Libraries
Keras wraps numerical computation libraries Theano and TensorFlow so you can define and train neural networks in minimum lines of code.
Reason 5. Uses of Keras
Keras can be used in many areas. Let me walk you through its uses in brief:
Reason 6. Deep Models on Smartphones
Keras is used to productize deep models on smartphones. Usually, deep models require lots of computation. Keras makes it easier to deep model a product that can be sold and executed on smartphones.
Reason 7. Distributed Training of DL Models
Distributed training means we can split a Deep Learning model into different parts and train it on different systems regardless of their locations. This way, a Deep Learning model can be trained much faster.
Summing it Up:
Deep Learning is not the future but the present. And the better we can utilize libraries like Kera while creating Deep Learning models, the more capable our Machine Learning algorithms will be. And ultimately, those machines will be able to fulfill their objectives with minimum training.
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