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Author : Dr.Syed Ahad Murtaza Alvi
Publisher : JEC PUBLICATION
ISBN 13 : 9357498184
Total Pages : 242 pages
Book Rating : 4.3/5 (574 download)

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Download or read book LEARNING INPUT OUTPUT FUNCTIONS OF MACHINE LEARNING written by Dr.Syed Ahad Murtaza Alvi and published by JEC PUBLICATION. This book was released on with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: A significant amount of different data is currently being generated all over the world as a result of the advancements that are currently taking place in the field of information technology. This data is being generated by means of social networking websites such as Facebook, Instagram, and Google Plus, amongst others, as well as electronic devices that are used by people, such as sensors. In addition, this data is being generated by sensors that are embedded in electronic devices. These statistics are accessible on social networking websites such as Facebook, Instagram, and Google Plus, amongst others. If the freshly created data are not initially sorted into the right categories, then they are merely meaningless rubbish until that moment. Because the data needed to be sorted into specific categories, there was a significant increase in the demand for data filtering and analytics. This was caused by the fact that the data needed to be categorised. The newly created data has huge volumes of a broad array of properties, as well as massive dimensions. In order to escape the "curse of dimensionality" and to construct a more effective machine learning (ML) model, it is required to transform high-dimensional data into low-dimensional data. This is done by reducing the number of variables in the data set. This will also make it possible for the data to be evaluated in a more straightforward manner. In order to successfully fulfil the task of categorization, a model that makes use of machine learning must be constructed. The data are then labelled as a direct consequence of this development. A framework for processing the data with machine learning and deep learning algorithms was given early on in the course of this research. This structure had components that were relevant to both the text and the visuals. The purpose of developing this framework was to make the task simpler and more straightforward to carry out. We made use of the ML and DL framework throughout the second stage of the research in order to exhibit data-related behaviours. The work that is being recommended is considered generic since it makes use of ML classifiers and DL classifiers in conjunction with FS techniques and FE techniques. In other words, it employs both FS techniques and FE approaches. To put it another way, it makes use of ML classifiers as well as DL classifiers in a general sense. After applying the algorithms to the datasets in a meticulous and systematic manner, we next examined the findings by placing a focus on accuracy as the primary criterion by which we judged the performance of the system.