000			02837nam a2200373 i 4500
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003			MnU
005			20241120064034.0
006			m o d s
007			cr
008			231021s2023 mnu o 0 0 eng d
020			_a9789403430270
040			_aMnU _beng _cMnU
050		4	_aQA76
100	1		_aBiehl, Michael _eauthor
245	0	4	_aThe Shallow and the Deep _bA biased introduction to neural networks and old school machine learning _cMichael Biehl
264		2	_aMinneapolis, MN _bOpen Textbook Library
264		1	_aGroningen, Netherlands _bUniversity of Groningen Press _c2023.
264		4	_c©2023.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	0		_aOpen textbook library.
505	0		_aPreface -- From neurons to networks -- Learning from example data -- The Perceptron -- Beyond linear separability -- Feed-forward networks for regression and classification -- Distance-based classifiers -- Model evaluation and regularization -- Preprocessing and unsupervised learning -- Concluding quote -- Appendix A: Optimization -- List of figures -- List of algorithms -- Abbrev. and acronyms -- Bibliography
520	0		_aThe Shallow and the Deep is a collection of lecture notes that offers an accessible introduction to neural networks and machine learning in general. However, it was clear from the beginning that these notes would not be able to cover this rapidly changing and growing field in its entirety. The focus lies on classical machine learning techniques, with a bias towards classification and regression. Other learning paradigms and many recent developments in, for instance, Deep Learning are not addressed or only briefly touched upon. Biehl argues that having a solid knowledge of the foundations of the field is essential, especially for anyone who wants to explore the world of machine learning with an ambition that goes beyond the application of some software package to some data set. Therefore, The Shallow and the Deep places emphasis on fundamental concepts and theoretical background. This also involves delving into the history and pre-history of neural networks, where the foundations for most of the recent developments were laid. These notes aim to demystify machine learning and neural networks without losing the appreciation for their impressive power and versatility.
542	1		_fAttribution-NonCommercial-ShareAlike
546			_aIn English.
588	0		_aDescription based on online resource
650		0	_aComputer Science _vTextbooks
650		0	_aArtificial Intelligence _vTextbooks
710	2		_aOpen Textbook Library _edistributor
856	4	0	_uhttps://open.umn.edu/opentextbooks/textbooks/1516 _zAccess online version
999			_c39652 _d39652