000			01111nam a22001457a 4500
005			20250307093027.0
008			250307b |||||||| |||| 00| 0 eng d
020			_a978-0-262-03561-3
082			_223 _a006.31 _bGOO
100			_aGoodfellow Ian
245			_aDeep Learning: _cIan Goodfellow, Yoshau Bengio and Aaron Courville _henglish
300			_avi,775 p. ; _bhard bound _c8x23 cm
505			_aPart I: Applied Math and Machine Learning Basics 2 Linear Algebra 3 Probability and Information Theory 4 Numerical Computation 5 Machine Learning Basics Part II: Modern Practical Deep Networks 6 Deep Feedforward Networks 7 Regularization for Deep Learning 8 Optimization for Training Deep Models 9 Convolutional Networks 10 Sequence Modeling: Recurrent and Recursive Nets 11 Practical Methodology 12 Applications Part III: Deep Learning Research 13 Linear Factor Models 14 Autoencoders 15 Representation Learning 16 Structured Probabilistic Models for Deep Learning 17 Monte Carlo Methods 18 Confronting the Partition Function 19 Approximate Inference 20 Deep Generative Models
942			_2ddc _cBK
999			_c9616 _d9616