000 | 01899cam a2200361 i 4500 | ||
---|---|---|---|
001 | 19134018 | ||
003 | OSt | ||
005 | 20221227081204.0 | ||
008 | 160613t20162016maua b 001 0 eng | ||
010 | _a 2016022992 | ||
020 | _a9780262035613 | ||
040 |
_aDLC _beng _cDLC _erda _dDLC |
||
042 | _apcc | ||
050 | 0 | 0 |
_aQ325.5 _b.G66 2016 |
082 | 0 | 0 |
_a006.31 _222 |
100 | 1 |
_aGoodfellow, Ian, _eauthor. |
|
245 | 1 | 0 |
_aDeep learning _cIan Goodfellow, Yoshua Bengio, and Aaron Courville. |
264 | 1 |
_aCambridge, Massachusetts : _bThe MIT Press, _c[2016] |
|
264 | 4 | _c©2016 | |
300 |
_a775 p. _billustrations (some color) ; _c24 cm. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_aunmediated _bn _2rdamedia |
||
338 |
_avolume _bnc _2rdacarrier |
||
490 | 0 | _aAdaptive computation and machine learning | |
504 | _aIncludes bibliographical references (pages 711-766) and index. | ||
505 | 0 | _aApplied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- Optimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Structured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models. | |
650 | 0 | _aMachine learning, | |
700 | 1 |
_aBengio, Yoshua, _eauthor. |
|
700 | 1 |
_aCourville, Aaron, _eauthor. |
|
906 |
_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg |
||
942 |
_2ddc _cBK _n0 |
||
999 |
_c30684 _d30684 |