Deep Learning: Ian Goodfellow, Yoshau Bengio and Aaron Courville english

By:

Goodfellow Ian

Material type: Text

TextDescription: vi,775 p. ; hard bound 8x23 cmISBN:

978-0-262-03561-3

DDC classification:

23 006.31 GOO

Contents:

Part 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

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Books	Tetso College Library Computer Science	Non-fiction	006.31 GOO (Browse shelf(Opens below))	Available		14538

Browsing Tetso College Library shelves, Shelving location: Computer Science, Collection: Non-fiction Close shelf browser (Hides shelf browser)

Previous					No cover image available			Next
Previous	006.3 RUS Artificial Intelligence: A modern Approach/	006.3 RUS Artificial Intelligence ; A modern Approach /	006.30922 MET Genius Makers The Mavericks Who Brought AI to Google,Facebook and the World	006.31 GOO Deep Learning:	006.31 JOH machine learning:/	006.35 VAJ Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems/	006.6 HEA Computer Graphics C Version:/	Next

Part 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

There are no comments on this title.

to post a comment.