008 |
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160613s2016 maua b 001 0 eng |
010 |
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|a 2016022992
|
020 |
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|a9780262035613 (hbk.) |cUSD80.00
|
020 |
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|a0262035618
|
040 |
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|aDLC|beng|cDLC|dDLC|dNOU
|
042 |
|
|apcc
|
050 |
00
|
|aQ325.5|b.G66 2016
|
082 |
00
|
|a006.3/1|223
|
095 |
|
|aLB|bLBF|cE020159|dQ325.5|e.G651|y2016|fWJ|n2176|pBook|tLCC
|
100 |
1
|
|aGoodfellow, Ian.
|
245 |
10
|
|aDeep learning /|cIan Goodfellow, Yoshua Bengio and Aaron Courville.
|
260 |
|
|aCambridge, Massachusetts :|bThe MIT Press,|cc2016.
|
300 |
|
|axxii, 775 p. :|bill. (some col.) ;|c24 cm.
|
490 |
0
|
|aAdaptive computation and machine learning
|
504 |
|
|aIncludes bibliographical references (p. [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.
|
700 |
1
|
|aCourville, Aaron.
|