| 008 |
|
160613s2016 maua b 001 0 eng |
| 010 |
|
|a 2016022992
|
| 020 |
|
|a9780262035613 (hbk.) |cUSD80.00
|
| 020 |
|
|a0262035618
|
| 040 |
|
|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.
|
Check-outs
: 
Hit：547
Rating：1
Review：0
Trackback：0
