Advantages and Disadvantages of Tensorflow
What is Tensorflow?
It is a framework used for machine learning and deep learning, familiarly called Google’s Tensorflow.
Advantages of Tensorflow
The advantages of using Tensorflow are given below:
1. Platform Independent
Tensorflow provides the feature that it is an open-source platform for every user. It has wide library communities, which helps the new ones learn the technology easily and quickly. By utilizing this as an advantage, the learners can easily learn the application, which helps them develop and deploy the application.
2. Graphic and Architectural support
The architecture and graphical support play vital support in TensorFlow. It supports fast computation and helps deploy in the cloud easily. Using a graph, one can easily develop the neural network using nodes. It is used for image and voice recognition. Hence graph plays a vital role in Tensorflow applications.
3. Supports multiple languages
4. Visualization of Data
Visualization means presenting the information in the form of an image. Another meaning involves mental image, which resembles the same visual perception. This visualization helps to find and rectify the error, reducing time overuse in finding the error.
5. Keras usage in Tensorflow
Tensorflow supports Keras library functionality used to perform some high-level functionality. It helps with executing different topologies of input and output layers.
Scalability means it allows programmers to do any operation. Hence, it helps develop various systems, including Intel, Snapchat, Dropbox, and Airbnb. It helps the user by deploying on any machine.
The disadvantages of Tensorflow are given below:
1. Lack of OS Support
Tensorflow supports Linux operating systems rather than Windows OS. Hence Windows OS users face difficulty while using. The features of Tensorflow are less when compared to Linux Operating System.
2. Fast updation
Tensorflow frequently updates, which is difficult to handle by the user for installation purposes and merge with the existing method or system.
3. Execution Dependency
Tensorflow helps to reduce the length of the code, which also requires various platforms for its execution. Hence program execution requires dependency on the platform.
4. Speed is Slow
It is reported that the Tensorflow framework is slow, rather than other frameworks.
Hence in the above method, the advantage and disadvantages of Tensorflow are described. Even though there exist some disadvantages, it has some advantages also. Necessary methods or steps should take to reduce the cons of Tensorflow.
Algorithm used in Tensorflow
The algorithm used in Tensorflow is Linear Regression, Deep learning classification, Booster tree regression, Booster tree classification, Deep learning wipe, and deep Classification.
Why is Tensorflow used?
It involves the train’s working process, building and estimating the model. The input is represented in a graph, and the output is obtained after various multiple computational processes. This Tensorflow runs in various OS, including Linux, Windows, and macOS. It is subdivided into the desktop, mobile, and Cloud.
The major components of Tensorflow include tensors and graphs. This Tensorflow library framework is a familiar one where it an open-source to every user. It is famous for deep learning.
The data are stored in Tensorflow by two methods. Either it is stored in memory or a pipeline. Storing the data in memory is an easier and simpler method. The data pipelines are used to store an enormous amount of data.
Various steps exist to create a pipeline, including data creation, creation of placeholder, dataset defining method, and finally, the pipeline is created. Google uses TensorFlow for its application, including Gmail, Photo, and Google Search Engine.
Hence in the above article, the various pros and cons of Tensorflow are discussed. It plays a vital role in deep learning and machine learning. The tensor flow framework is a familiar one used by software engineers.