Cart(1)
Sale!

AN INTRODUCTION TO DEEP LEARNING

Vibhor Kumar Vishnoi received the B.Tech. and M.Tech. degrees in Computer Science and Engineering from UP Technical University, Lucknow, India, in 2012 and 2014, respectively, pursuing the Ph.D. degree in computer science with Gurukula Kangri (Deemed to be University), Haridwar, India. He is currently with the College of Computing Sciences and Information Technology, Teerthanker Mahaveer University, Moradabad, India as an Assistant Professor. His research interests include image processing, pattern recognition, precision agriculture, machine vision, and deep learning.

Dr. Nupa Ram Chauhan working as an Associate Professor in the Computer Science and Engineering Department at Teerthanker Mahaveer University Moradabad, UP. India. He has completed his Ph.D. from Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh Lucknow, U.P. in Computer Science and Engineering. Previously he has been working as an Associate Professor and Head of the Department of Computer Science & Engineering in FGIET Raebareli. His area of specialization is Database System, Distributed Real-Time Systems, and Artificial Intelligence. He is a life member of ISTE and a nominated member of the Computer Society of India. His several research and conference proceedings have been published in National and International Journals.

Dr. Krishan Kumar, working as an Assistant Professor in Department of Computer science, Faculty of Science, Gurukula Kangri (Deemed to be University), Haridwar, Uttarakhand, India-249401. He received the B.Sc. degree in mathematics from MJP Rohilkhand University, Bareilly, India, in 1997, the Master of Computer Application degree from CCS University, Meerut, India, in 2001, and the Ph.D. degree in computer science and information technology from the Institute of Engineering and Technology, MJP Rohilkhand University, in 2010. He is currently working as an Assistant Professor (level 12) with the Department of Computer Science, Gurukula Kangri (Deemed to be University), Haridwar, India. Moreover, he is having 20 years of experience in academics. He has published more than 40 research papers in various national and international journals and proceedings. Furthermore, he has written three books and two book chapters. His research interests include deep learning, natural language processing, image processing, and precision agriculture.

 

Description

In profound learning, a counterfeit brain organization (ANN) stores and cycles a lot of information. This is on the grounds that fake brain networks are utilized in profound learning. It can track down both plain and secretive associations across datasets. While working with profound learning, direct writing computer programs isn’t required all the time. With regards to the human body, the mind is for certain quite possibly of the most wonderful part. It is workable for every one of these faculties to be impacted by the natural inclinations that we as a whole have. On a basic level, brain organizations could inexact any capability precisely, no matter what the capability’s intricacy. In any case, by utilizing directed figuring out how to gain a capability that maps one X to another Y, choosing the best Y for an original X is conceivable. One of the subfields that falls under the umbrella of AI is known as convolutional brain organizations (frequently shortened as CNNs or convnets). It is one of the numerous unmistakable sorts of counterfeit brain organizations, which are all ready to deal with various types of information for many applications. Profound learning is an area of AI that spotlights on training counterfeit brain organizations to complete specific capabilities all alone. Computational models that follow the association and activity of the human cerebrum are alluded to as brain organizations. Profound learning frameworks are developed utilizing prescient demonstrating and factual investigation filling in as the establishments whereupon they are constructed. Working on the exhibition of a model might be testing and is generally founded on the sort of information that is being utilized as well as the preparation of the model, which includes the hyperparameters being set to their ideal qualities. Playing out an evaluation of profound learning models is vital for every single application, and the reason for this section is to give a portrayal of the presentation measurements that are related with this assessment. The significant foci of AI are the encoding of the info information and the speculation of the learnt designs for use to future information that has not yet been seen. Both of these cycles are crucial for the course of AI.

Reviews

There are no reviews yet.

Add a review

Your email address will not be published. Required fields are marked *