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05398nam a22004693i 4500 |
| 001 - CONTROL NUMBER |
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EBC5573417 |
| 003 - CONTROL NUMBER IDENTIFIER |
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MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20220331084434.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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220328s2018 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781788624534 |
| Qualifying information |
(electronic bk.) |
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| Cancelled/invalid ISBN |
9781788629416 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(MiAaPQ)EBC5573417 |
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| System control number |
(Au-PeEL)EBL5573417 |
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| System control number |
(CaPaEBR)ebr11630312 |
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| System control number |
(OCoLC)1065140940 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
MiAaPQ |
| Language of cataloging |
eng |
| Description conventions |
rda |
| -- |
pn |
| Transcribing agency |
MiAaPQ |
| Modifying agency |
MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.73.P98 .A854 2018 |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.133 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Atienza, Rowel. |
| 245 10 - TITLE STATEMENT |
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| Title |
Advanced Deep Learning with Keras : |
| Remainder of title |
Apply Deep Learning Techniques, Autoencoders, GANs, Variational Autoencoders, Deep Reinforcement Learning, Policy Gradients, and More. |
| 264 #1 - |
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| -- |
Birmingham : |
| -- |
Packt Publishing, Limited, |
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2018. |
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| -- |
�2018. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource (369 pages) |
| 336 ## - |
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text |
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txt |
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rdacontent |
| 337 ## - |
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computer |
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c |
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rdamedia |
| 338 ## - |
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online resource |
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cr |
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rdacarrier |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Cover -- Copyright -- Packt upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introducing Advanced Deep Learning with Keras -- Why is Keras the perfect deep learning library? -- Installing Keras and TensorFlow -- Implementing the core deep learning models - MLPs, CNNs and RNNs -- The difference between MLPs, CNNs, and RNNs -- Multilayer perceptrons (MLPs) -- MNIST dataset -- MNIST digits classifier model -- Building a model using MLPs and Keras -- Regularization -- Output activation and loss function -- Optimization -- Performance evaluation -- Model summary -- Convolutional neural networks (CNNs) -- Convolution -- Pooling operations -- Performance evaluation and model summary -- Recurrent neural networks (RNNs) -- Conclusion -- Chapter 2: Deep Neural Networks -- Functional API -- Creating a two-input and one-output model -- Deep residual networks (ResNet) -- ResNet v2 -- Densely connected convolutional networks (DenseNet) -- Building a 100-layer DenseNet-BC for CIFAR10 -- Conclusion -- References -- Chapter 3: Autoencoders -- Principles of autoencoders -- Building autoencoders using Keras -- Denoising autoencoder (DAE) -- Automatic colorization autoencoder -- Conclusion -- References -- Chapter 4: Generative Adversarial Networks (GANs) -- An overview of GANs -- Principles of GANs -- GAN implementation in Keras -- Conditional GAN -- Conclusion -- References -- Chapter 5: Improved GANs -- Wasserstein GAN -- Distance functions -- Distance function in GANs -- Use of Wasserstein loss -- WGAN implementation using Keras -- Least-squares GAN (LSGAN) -- Auxiliary classifier GAN (ACGAN) -- Conclusion -- References -- Chapter 6: Disentangled Representation GANs -- Disentangled representations -- InfoGAN -- Implementation of InfoGAN in Keras -- Generator outputs of InfoGAN -- StackedGAN -- Implementation of StackedGAN in Keras. |
|
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| Formatted contents note |
Generator outputs of StackedGAN -- Conclusion -- Reference -- Chapter 7: Cross-Domain GANs -- Principles of CycleGAN -- The CycleGAN Model -- Implementing CycleGAN using Keras -- Generator outputs of CycleGAN -- CycleGAN on MNIST and SVHN datasets -- Conclusion -- References -- Chapter 8: Variational Autoencoders (VAEs) -- Principles of VAEs -- Variational inference -- Core equation -- Optimization -- Reparameterization trick -- Decoder testing -- VAEs in Keras -- Using CNNs for VAEs -- Conditional VAE (CVAE) -- -VAE: VAE with disentangled latent representations -- Conclusion -- References -- Chapter 9: Deep Reinforcement Learning -- Principles of reinforcement learning (RL) -- The Q value -- Q-Learning example -- Q-Learning in Python -- Nondeterministic environment -- Temporal-difference learning -- Q-Learning on OpenAI gym -- Deep Q-Network (DQN) -- DQN on Keras -- Double Q-Learning (DDQN) -- Conclusion -- References -- Chapter 10: Policy Gradient Methods -- Policy gradient theorem -- Monte Carlo policy gradient (REINFORCE) method -- REINFORCE with baseline method -- Actor-Critic method -- Advantage Actor-Critic (A2C) method -- Policy Gradient methods with Keras -- Performance evaluation of policy gradient methods -- Conclusion -- References -- Other Books You May Enjoy -- Index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
This book covers advanced deep learning techniques to create successful AI. Using MLPs, CNNs, and RNNs as building blocks to more advanced techniques, you'll study deep neural network architectures, Autoencoders, Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), and Deep Reinforcement Learning (DRL) critical to many. |
| 588 ## - |
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| -- |
Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) |
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| Local note |
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Python (Computer program language). |
|
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| Topical term or geographic name as entry element |
Neural networks (Computer science). |
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| Topical term or geographic name as entry element |
Machine learning. |
| 655 #4 - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
Electronic books. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
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| Display text |
Print version: |
| Main entry heading |
Atienza, Rowel |
| Title |
Advanced Deep Learning with Keras |
| Place, publisher, and date of publication |
Birmingham : Packt Publishing, Limited,c2018 |
| International Standard Book Number |
9781788629416 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) |
|---|
| Corporate name or jurisdiction name as entry element |
ProQuest (Firm) |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
https://ebookcentral.proquest.com/lib/kliuc-ebooks/detail.action?docID=5573417 |
| Public note |
Click to View |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Koha item type |
E-book |
