Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play (Record no. 149039)

MARC details
000 -LEADER
fixed length control field 02535nam a22002657a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20241016114243.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241016b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355429988
040 ## - CATALOGING SOURCE
Transcribing agency
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Item number FOS
Classification number 006.31
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Foster, David
9 (RLIN) 121259
245 ## - TITLE STATEMENT
Title Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
250 ## - EDITION STATEMENT
Edition statement 2nd Ed
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc. Mumvai
Name of publisher, distributor, etc. Shroff Publishers & Distributors Pvt. Ltd.
Date of publication, distribution, etc. 2023
300 ## - PHYSICAL DESCRIPTION
Extent 426p
500 ## - GENERAL NOTE
General note I. Introduction to Generative Deep Learning<br/>1. Generative Modeling<br/>1. What Is Generative Modeling?<br/>2. Probabilistic Generative Models<br/>3. The Challenges of Generative Modeling<br/>4. Setting Up Your Environment<br/>5. Summary<br/>2. Deep Learning<br/>1. Structured and Unstructured Data<br/>2. Deep Neural Networks<br/>3. Your First Deep Neural Network<br/>4. Improving the Model<br/>3. Variational Autoencoders<br/>1. The Art Exhibition<br/>2. Autoencoders<br/>3. The Variational Art Exhibition<br/>4. Building a Variational Autoencoder<br/>5. Using VAEs to Generate Faces<br/>4. Generative Adversarial Networks<br/>1. Ganimals<br/>2. Introduction to GANs<br/>3. Your First GAN<br/>4. GAN Challenges<br/>5. Wasserstein GAN<br/>6. WGAN-GP<br/>7. Summary<br/>II. Teaching Machines to Paint, Write, Compose, and Play<br/>5. Paint<br/>1. Apples and Organges<br/>2. CycleGAN<br/>3. Your First CycleGAN<br/>4. Creating a CycleGAN to Paint Like Monet<br/>5. Neural Style Transfer<br/>6. Write<br/>1. The Literary Society for Troublesome Miscreants<br/>2. Long Short-Term Memory Networks<br/>3. Your First LSTM Network<br/>4. Generating New Text<br/>5. RNN Extensions<br/>6. Encoder–Decoder Models<br/>7. A Question and Answer Generator<br/>7. Compose<br/>1. Preliminaries<br/>2. Your First Music-Generating RNN<br/>3. The Musical Organ<br/>4. Your First MuseGAN<br/>5. The MuseGAN Generator<br/>6. The Critic<br/>7. Analysis of the MuseGAN<br/>8. Play<br/>1. Reinforcement Learning<br/>2. World Model Architecture<br/>3. Setup<br/>4. Training Process Overview<br/>5. Collecting Random Rollout Data<br/>6. Training the VAE<br/>7. Collecting Data to Train the RNN<br/>8. Training the MDN-RNN<br/>9. Training the Controller<br/>10. In-Dream Training<br/>9. The Future of Generative Modeling<br/>1. Five Years of Progress<br/>2. The Transformer<br/>3. Advances in Image Generation<br/>4. Applications of Generative Modeling
600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME
Personal name Machine Learning
9 (RLIN) 121260
600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME
Personal name Generative Adversarial Networks
9 (RLIN) 121261
600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME
Personal name Encoder-decoder Models
9 (RLIN) 121262
600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME
Personal name CycleGAN
9 (RLIN) 121263
600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME
Personal name ProGAN
9 (RLIN) 121264
600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME
Personal name StyleGAN
9 (RLIN) 121265
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Call number suffix FOS
Call number prefix 006.31
Source of classification or shelving scheme Dewey Decimal Classification

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