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02345cam a2200409ua 4500 |
| 001 |
19134018 |
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20191212161848.0 |
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180222t20162016maua b 001 0 eng |
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|a 2016022992
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| 020 |
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|a 0262035618 (hardcover : alk. paper)
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| 020 |
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|a 9780262035613 (hardcover : alk. paper)
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| 040 |
|
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|a DLC
|b eng
|c DLC
|d DLC
|e rda
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|a eng
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|a pcc
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|b .G66 2016
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|2 23
|a 006.31
|
| 100 |
1 |
|
|a Goodfellow, Ian,
|e author.
|
| 245 |
|
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|a Deep learning /
|c Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
|h Textual Documents
|
| 264 |
|
1 |
|a Cambridge, Massachusetts :
|b The MIT Press,
|c [2016]
|
| 264 |
|
4 |
|c ?2016
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| 300 |
|
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|a xxii, 775 pages :
|b illustrations (some color) ;
|c 24 cm.
|
| 336 |
|
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|2 rdacontent
|a text
|b txt
|
| 337 |
|
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|2 rdamedia
|a unmediated
|b n
|
| 338 |
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|2 rdacarrier
|a volume
|b nc
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| 490 |
|
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|a Adaptive computation and machine learning
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| 504 |
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|a Includes bibliographical references (pages 711-766) and index.
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| 505 |
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|a Applied 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.
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| 650 |
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|a Machine learning,
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| 700 |
|
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|a Bengio, Yoshua,
|e author..
|e author.
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| 700 |
|
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|a Courville, Aaron,
|e author.
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| 852 |
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|a acquire
|b 1 shelf copy
|x policy default
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| 942 |
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|c BK
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| 955 |
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|a xn12 2017-02-24 1 copy rec`d., to CIP ver.
|b rk14 2016-06-13
|c rk14 2016-06-13 telework to subj
|d rk09 2016-08-03 to Dewey
|f rl09 2017-03-10 ; to CALM
|w xm06 2016-08-04
|a rl46 2017-03-07 to SMA
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