000			01829nam a2200229Ia 4500
003			NULRC
005			20250520103029.0
008			250520s9999 xx 000 0 und d
020			_a9780262537551
040			_cNULRC
050			_aQ 325.5 .K45 2019
100			_aKelleher, John D. _eauthor
245		0	_aDeep learning / _cJohn D. Kelleher
260			_aCambridge, Massachusetts : _bThe MIT Press, _cc2019
300			_ax, 280 pages : _billustrations ; _c18 cm.
365			_bUSD12
504			_aIncludes bibliographical references.
505			_a1. Introduction to Deep Learning -- 2. Conceptual Foundations -- 3. Neural Networks: The Building Blocks of Deep Learning -- 4. A Brief History of Deep Learning -- 5. Convolutional and Recurrent Neural Networks -- 6. Learning Functions -- 7. The Future of Deep Learning.
520			_aArtificial Intelligence is a disruptive technology across business and society. There are three long-term trends driving this AI revolution: the emergence of Big Data, the creation of cheaper and more powerful computers, and development of better algorithms for processing and learning from data. Deep learning is the subfield of Artificial Intelligence that focuses on creating large neural network models that are capable of making accurate data driven decisions. Modern neural networks are the most powerful computational models we have for analyzing massive and complex datasets, and consequently deep learning is ideally suited to take advantage of the rapid growth in Big Data and computational power. In the last ten years, deep learning has become the fundamental technology in computer vision systems, speech recognition on mobile phones, information retrieval systems, machine translation, game AI, and self-driving cars.
650			_aARTIFICIAL INTELLIGENCE
942			_2lcc _cBK
999			_c21784 _d21784