000			04170cam a2200601 i 4500
001			on1314853913
003			OCoLC
005			20240523125544.0
006			m o d
007			cr cnu---unuuu
008			220508t20222022njua ob 001 0 eng d
040			_aYDX _beng _erda _cYDX _dYDX _dOCLCQ _dDG1 _dOCLCF _dORMDA _dOCLCQ _dOCLCO
020			_a9781119792437 _q(electronic book)
020			_a1119792436 _q(electronic book)
020			_a9781119792413 _q(electronic book)
020			_a111979241X _q(electronic book)
020			_z1119791758 _q(hardcover)
020			_z9781119791751 _q(hardcover)
029	1		_aAU@ _b000073207946
035			_a(OCoLC)1314853913
037			_a9781119791751 _bO'Reilly Media
050		4	_aQ325.73 _b.A38 2022
082	0	4	_a006.3/1 _223/eng/20220511
049			_aMAIN
245	0	0	_aAdvanced analytics and deep learning models / _cedited by Archana Mire, Shaveta Malik and Amit Kumar Tyagi.
264		1	_aHoboken, NJ : _bJohn Wiley & Sons, _c2022.
264		4	_c�2022
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
504			_aIncludes bibliographical references and index.
588	0		_aOnline resource; title from digital title page (viewed on May 11, 2022).
520			_aAdvanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.
590			_aJohn Wiley and Sons _bWiley Online Library: Complete oBooks
650		0	_aDeep learning (Machine learning)
650		0	_aArtificial intelligence.
650		0	_aBig data.
650		6	_aApprentissage profond.
650		6	_aIntelligence artificielle.
650		6	_aDonn�ees volumineuses.
650		7	_aartificial intelligence. _2aat
650		7	_aArtificial intelligence _2fast
650		7	_aBig data _2fast
650		7	_aDeep learning (Machine learning) _2fast
700	1		_aMire, Archana, _eeditor.
700	1		_aMalik, Shaveta, _eeditor.
700	1		_aTyagi, Amit Kumar, _eeditor.
776	0	8	_iPrint version: _z1119791758 _z9781119791751 _w(OCoLC)1252050379
856	4	0	_uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119792437
938			_aYBP Library Services _bYANK _n302872328
994			_a92 _bINLUM
999			_c12886 _d12886