000			04170nam a22003734u 4500
001			10.1002/9781394205639
003			Wiley
005			20251030120317.0
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008			231127t2023||||xxu fs| |0|| 0|eng||
020			_a9781394205639 _qelectronic
024	7		_a10.1002/9781394205639 _2doi
041			_aeng
100	1		_aSantry, Douglas J. _4aut _992115
245	1	0	_aDemystifying Deep Learning _bAn Introduction to the Mathematics of Neural Networks _h[electronic resource] _cDouglas J. Santry
250			_a1
260			_bWiley _c2023
264		1	_bWiley _c2023
300			_a256pp.;
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _2rdaft
347			_bPDF
520			_aDEMYSTIFYING DEEP LEARNING Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science, for example. Just as the robot revolution threatened blue-collar jobs in the 1970s, so now the AI revolution promises a new era of productivity for white collar jobs. Important tasks have begun being taken over by ANNs, from disease detection and prevention, to reading and supporting legal contracts, to understanding experimental data, model protein folding, and hurricane modeling. AI is everywhere-on the news, in think tanks, and occupies government policy makers all over the world -and ANNs often provide the backbone for AI. Relying on an informal and succinct approach, Demystifying Deep Learning is a useful tool to learn the necessary steps to implement ANN algorithms by using both a software library applying neural network training and verification software. The volume offers explanations of how real ANNs work, and includes 6 practical examples that demonstrate in real code how to build ANNs and the datasets they need in their implementation, available in open-source to ensure practical usage. This approachable book follows ANN techniques that are used every day as they adapt to natural language processing, image recognition, problem solving, and generative applications. This volume is an important introduction to the field, equipping the reader for more advanced study. Demystifying Deep Learning readers will also find: A volume that emphasizes the importance of classification Discussion of why ANN libraries, such as Tensor Flow and Pytorch, are written in C++ rather than Python Each chapter concludes with a "Projects" page to promote students experimenting with real code A supporting library of software to accompany the book at https://github.com/nom-de-guerre/RANT An approachable explanation of how generative AI, such as generative adversarial networks (GAN), really work. An accessible motivation and elucidation of how transformers, the basis of large language models (LLM) such as ChatGPT, work. Demystifying Deep Learning is ideal for engineers and professionals that need to learn and understand ANNs in their work. It is also a helpful text for advanced undergraduates to get a solid grounding on the topic.
650		4	_aNeural Networks _991501
650		4	_aArtificial Intelligence _958441
650		4	_aComputer-Aided Engineering _966003
653	0	0	_aAI; artificial intelligence; machine learning; Generative AI; Transformers; Natural Language Processing; Convolutional Neural Networks (CNN); classification; regression; data science; supervised learning; medicine; law; financial services; science; natural language process; image recognition; problem solving; generative applications
776	0	8	_iPrinted edition: _z9781394205608
856	4	0	_uhttps://onlinelibrary.wiley.com/action/showBook?doi=10.1002%2F9781394205639 _aWiley Online Library
856	4	2	_3Cover Image _uhttps://onlinelibrary.wiley.com/cms/asset/5f487756-4062-4f07-8ebf-187e4ab2b53c/9781394205639.cover.gif _aWiley Online Library
999			_c67223 _d67223