000			01995nam a22003377i 4500
005			20251030135754.0
007			t|
008			220601s2016 mau||||| |||| 00| 0 spa d
020			_a9780262035613
040			_aCO-ViULL _erda _bspa
041	0		_aeng
082	0		_221 _a006.3 _bG651d
100	1		_aGoodfellow, Ian _9157742, _eautor. _4aut
245	0	0	_aDeep Learning / _cIan Goodfellow, Yoshua Bengio, and Aaron Courville.
264		1	_aCambridge, Massachusetts ; _aLondon : _bThe MIT Press, _c2016.
300			_axvii, 775 páginas (algunas a color) : _bilustraciones ; _c24 centímetros .
336			_atexto _btxt _2rdacontent
337			_asin mediación _bn _2rdamedia
338			_avolumen _bnc _2rdacarrier
490	1		_aAdaptive computation and machine learning
500			_aIncluye bibliografía e índice.
505	2		_aApplied Math and machine learning basics -- Linear algebra -- Probability and information theory -- Numerical computation -- Mchine learning basics -- Deep networks: modern practices -- Deep feedforward networks -- Regularization for deep lerning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research: Linear factors models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Mote Carlo methods -- Confronting the partition Function -- Appoximate Inference -- Deep Generative models .
520	3		_aUna introducción a una amplia gama de temas en el aprendizaje profundo, que cubre antecedentes matemáticos y conceptuales, técnicas de aprendizaje profundo utilizadas en la industria y perspectivas de investigación.
650	1	7	_aDeep learning _9157743.
650	2	7	_aAlgebra lineal _9157744.
650	2	7	_aMachine learning _9157745.
650	2	7	_aRedes de computadores _9157746.
700	1		_aBengio, Yoshua _9157747.
700	1		_aCourville, Aaron _9157748.
942			_2ddc _cBK
999			_c46791 _d46791