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DOI: 10.2174/97898150792101230101 eISBN: 978-981-5079-21-0, 2023 ISBN: 978-981-5079-22-7
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For Books Gyanendra Verma, Rajesh Doriya , " Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing ", Bentham Science Publishers (2023). https://doi.org/10.2174/97898150792101230101
Print ISBN978-981-5079-22-7
Online ISBN978-981-5079-21-0
Page: i-i (1) Author: Shitala Prasad DOI: 10.2174/9789815079210123010001
Page: ii-iii (2) Author: Gyanendra Verma and Rajesh Doriya DOI: 10.2174/9789815079210123010002
Page: iv-v (2) Author: DOI: 10.2174/9789815079210123010003
Page: 1-18 (18) Author: Jaykumar Suraj Lachure*, Gyanendra Verma and Rajesh Doriya DOI: 10.2174/9789815079210123010004 PDF Price: $15
Page: 19-32 (14) Author: Sampurna Panda, Rakesh Kumar Dhaka and Babita Panda* DOI: 10.2174/9789815079210123010005 PDF Price: $15
Page: 33-46 (14) Author: Saleena Thorayanpilackal Sulaiman*, Muhamed Ilyas Poovankavil and Abdul Jabbar Perumbalath DOI: 10.2174/9789815079210123010006 PDF Price: $15
Page: 47-59 (13) Author: Sushila Ratre*, Nehha Seetharaman and Aqib Ali Sayed DOI: 10.2174/9789815079210123010007 PDF Price: $15
Page: 60-72 (13) Author: Jayalakshmi Ramachandran Nair*, Sumathy Pichai Pillai and Rajkumar Narayanan DOI: 10.2174/9789815079210123010008 PDF Price: $15
Page: 73-89 (17) Author: Trupthi Muralidharr*, Prajwal Sethu Madhav, Priyanka Prashanth Kumar and Harshawardhan Tiwari DOI: 10.2174/9789815079210123010009 PDF Price: $15
Page: 90-102 (13) Author: Srishti Sakshi Sinha and Uma Vijayasundaram* DOI: 10.2174/9789815079210123010010 PDF Price: $15
Page: 103-128 (26) Author: Natarajan Balasubramanian and Elakkiya Rajasekar* DOI: 10.2174/9789815079210123010011 PDF Price: $15
Page: 129-145 (17) Author: Elakkiya Rajasekar, Archana Mathiazhagan and Elakkiya Rajalakshmi* DOI: 10.2174/9789815079210123010012 PDF Price: $15
Page: 146-166 (21) Author: Harshee Pitroda*, Manisha Tiwari and Ishani Saha DOI: 10.2174/9789815079210123010013 PDF Price: $15
Page: 167-182 (16) Author: Anil Verma, Aman Singh*, Divya Anand and Rishika Vij DOI: 10.2174/9789815079210123010014 PDF Price: $15
Page: 183-205 (23) Author: Mayank Gupta and Poonam Saini* DOI: 10.2174/9789815079210123010015 PDF Price: $15
Page: 206-219 (14) Author: Jay Prajapati* and Siba Panda DOI: 10.2174/9789815079210123010016 PDF Price: $15
Page: 220-233 (14) Author: Sukrati Chaturvedi*, Chellapilla Vasantha Lakshmi and Patvardhan Chellapilla DOI: 10.2174/9789815079210123010017 PDF Price: $15
Page: 234-248 (15) Author: Vatsal Khandor*, Sanay Shah, Parth Kalkotwar, Saurav Tiwari and Sindhu Nair DOI: 10.2174/9789815079210123010018 PDF Price: $15
Page: 249-253 (5) Author: Gyanendra Verma and Rajesh Doriya DOI: 10.2174/9789815079210123010019
This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine-learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.
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