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    Keras Assignment Help Online | Keras Homework Help

    Neural network courses are in huge demand. Many universities and colleges started to introduce this course and few have added this to the computer science curriculum. There are different open-source libraries that are used to make neural networks work. Keras is one among them. We have a team of machine learning experts who hold in-depth knowledge and experience in completing the Keras assignment irrespective of its complexity level. We help students from across the globe by assisting them in writing assignments flawlessly. All our assignments will help them secure A+ grades in the examination.

    Deep learning is a part of artificial intelligence that solves complicated problems by working similar to that of the human brain. When you are doing deep learning, neural networks will be used which work with multiple operators that are placed on nodes to break the problems into smaller issues to solve them individually. It becomes easier to solve smaller issues with ease than to solve the bigger issue in one go. It is tough to implement a neural network and there comes the role of Keras. This is a kind of deep learning framework. We are the one-stop solution for all the Keras assignment help.


    What is Keras?

    Keras is a deep learning API that is developed by Google and is widely used to implement neural networks. The framework is written in Python and is primarily used to develop neural networks with ease. The best thing about this API is that it supports the computation of multiple neural networks in the backend. It is easy to learn and simple to work with. It offers a high level of abstraction in the front end and allows you to carry out a lot of computations in the back end. However, this framework is a bit slower when compared to the other deep learning frameworks and is friendly for even a beginner to work with. You can switch between various tasks in the backend using this library. 

    This open-source neural network library holds the capability to run on various types of frameworks such as TensorFlow, Theano, and CNTK. The library is user-friendly, easy to extend, and will allow you to experiment with and implement various neural networks. The best part of Keras is that it supports both convolutional networks as well as recurrent networks and a combination of these networks. The low-level computations that are tough to resolve can be resolved using the backend library. This library will act like an API wrapper that allows you to run TensorFlow, Theano, and CNTK. Learn and master all such concepts of Keras in a hassle-free and step-by-step manner from our Data Science experts.


    What makes this library unique?

    Keras is a kind of model-level library that is used to develop deep-learning models that offer you high-level building blocks. There is a special tensor manipulation library that is used to work as a backend engine. The best thing is that Keras instead of making use of a single library of Tensor and carrying out various operations related to that library offers various plugins for different backend engines to plug into Keras. 


    Various types of libraries that are supported by Keras include:

    • Tensorflow

    This is a Google product and is the widely used deep learning tool that allows you to carry out research on machine learning and deep neural networks. It is developed in a way that it can be run on GPU and CPU and various mobile operating systems. The wrappers that are made using C++, Java, Python, and other languages will be used. 

    • Theano

    It was developed in Canada and is an open-source Python library that helps you to carry out various mathematical operations using different arrays and by embedding numpy and Scipy. This uses GPUs to compute briskly and effectively by computing the gradients with the help of graphs that are built automatically. It is widely used for unsuitable expressions. It keenly observes the numerical part and later computes using stable algorithms. 

    • CNTK

    It is a deep learning yet open-source framework that has many building blocks that are necessary to build a neural network. You can easily train these models by using various programming languages, such as Python and C++ or Java for loading this model, which is used for making the right decisions in the business. 


    Few of the things that make this library unique:

    • Focus majorly on improving the user experience. 
    • Embraced by different industries
    • Support various platforms which help encoders to do coding
    • Easy to assimilate all the concepts
    • Easy to run on both GPU and CPU
    • Gives you the library to design architecture, which you can use as an API in any of the projects for later purpose
    • Simple to start off
    • Easy to come up with various models, which is what makes Keras highly special

    Our experts carry in-depth knowledge and practical experience in implementing such libraries. Submit your requirements and get instant Keras assignment help.


    User experience while using Keras

    • API designed and earmarked for humans

    There are different best practices that are embraced by Keras to reduce the cognitive load make sure that all the models are maintained consistently and keep the APIs simple. 

    • Not made for machines

    Keras will give you feedback when there is any error that has been encountered to reduce the human actions for most of the use cases. Simple for anyone to learn and make use of Keras

    • Offer ample flexibility 

    Keras offers enough flexibility for developers to integrate various deep learning languages like Theano and TensorFlow to ensure that anything that is coded in the base language is easier to implement in Keras. 


    Various advantages you can reap using Keras

    Companies are using this library to design the neural network as there are many benefits backed by Keras. A few of them include:

    • Simple for users to perceive and deploy the neural network model briskly
    • Has big community support, since many AI companies are using this library 
    • Support multi-backend. You can use any of the frameworks such as TensorFlow, Theano, and so on based on the requirement.
    • Easy to deploy and support cross-platform functionality 

    Significance of Keras

    • Keras is an API that is simple for anyone to learn. It is also made simple and comes with consistent APIs that will limit the number of actions taken to enforce the code. It also explains each of the user errors clearly. 
    • It takes less time for prototyping in Keras. It is easier for you to implement and deploy whatever ideas you have in no time. There is a wide range of deployment options that are offered by Keras based on the requirements of the user. 
    • Languages with abstraction at a high level and features that are built-in in the Keras are slow. It is also challenging to build customized features. However, using Keras with TensorFlow allows you to easily customize the workflows. 
    • The research community of Keras is highly developed. There is also extensive documentation available.
    • Many bigwigs across the globe have deployed products, which are developed with the help of Keras


    Various applications of Keras

    Following are a few of the applications of Keras:

    • Classification of Rock-paper-scissor 

    Keras is an open-source deep-learning library that makes use of the TensorFlow library. Games are an ideal way that promotes cognitive ability in kids and elders. In the same way, both deep learning and neural networks are thoroughly trained in simulating games. Take an example of a rock-paper-scissors game, where the convolutional neural network develops hand gestures for rock, paper, and scissors. The webcam equipped with the laptop will be used to identify the gestures of human beings based on which the game goes on. There is a pre-trained convolutional neural network called a squeeze net that is used to identify these gestures, which are categorized into three different categories such as rock, paper, and scissors

    • Image classification 

    Keras is useful in various applications, especially in image classification. Machine learning, deep learning, and neural networks are used to classify images. It gets trained based on the features and whenever the image matches with whatever is stored in the database, it pulls out the results. It is widely used in detecting gender and for doing surveillance checkups. There are two different libraries such as TensorFlow and Keras libraries that are used in classifying the images. Classification of images is applicable in various fields.

    • Computer vision

    There are many projects that are executed using Keras. There is a huge dataset that contains images with various expressions. The whole dataset is fed to different libraries such as TensorFlow, Keras, and OpenCV models. 

    • Face Mask detection

    There are many students who are trying to implement face mask detection using Keras, OpenCV, and TensorFlow. With this pandemic, people are wearing masks and it has become tough to find out whether or not the person is wearing a mask, especially in public places where there is a huge crowd. It is not possible to keep a human to track the individuals who are not wearing the mask and are violating the COVID regulations. There are Keras and OpenCV models that are used to detect whether or not the person is wearing a mask. There is a huge dataset of images that are fed to the system with and without masks of individuals. 

    • Generative Adversarial Network (GAN)

    It is a deep learning method that is used to get a new output from the datasets that are already in use. There is a machine learning modulation that is in use to generate new images by using the old images with the help of randomization. The capabilities of GAN come into the picture with its high dimensional data and to offer multiple answers giving multiple values. 

    • Heart disease classification

    Artificial intelligence and machine learning are two things that are widely used in the healthcare sector. Machine learning can be implemented accurately in the healthcare industry for data modeling. The classification of heart disease is carried out with various parameters such as diseases, genetic disorders, and with the help of eating habits. 

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