Hands-On Generative Adversarial Networks with Keras :: Your Guide to Implementing Next-Generation Generative Adversarial Networks.
This book will explore deep learning and generative models, and their applications in artificial intelligence. You will learn to evaluate and improve your GAN models by eliminating challenges that are encountered in real-world applications. You will implement GAN architectures in various domains suc...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This book will explore deep learning and generative models, and their applications in artificial intelligence. You will learn to evaluate and improve your GAN models by eliminating challenges that are encountered in real-world applications. You will implement GAN architectures in various domains such as computer vision, NLP, and audio processing. |
| Beschreibung: | 1 online resource (263 pages) |
| ISBN: | 9781789535136 1789535131 |
Internformat
MARC
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| 260 | |a Birmingham : |b Packt, |c 2019. | ||
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Foreword; Contributors; Table of Contents; Preface; Section 1: Introduction and Environment Setup; Chapter 1: Deep Learning Basics and Environment Setup; Deep learning basics; Artificial Neural Networks (ANNs); The parameter estimation; Backpropagation; Loss functions; L1 loss; L2 loss; Categorical crossentropy loss; Non-linearities; Sigmoid; Tanh; ReLU; A fully connected layer; The convolution layer; The max pooling layer; Deep learning environment setup; Installing Anaconda and Python; Setting up a virtual environment in Anaconda | |
| 505 | 8 | |a Installing TensorFlowInstalling Keras; Installing data visualization and machine learning libraries; The matplotlib library; The Jupyter library; The scikit-learn library; NVIDIA's CUDA Toolkit and cuDNN; The deep learning environment test; Summary; Chapter 2: Introduction to Generative Models; Discriminative and generative models compared; Comparing discriminative and generative models; Generative models; Autoregressive models; Variational autoencoders; Reversible flows; Generative adversarial networks; GANs -- building blocks; The discriminator; The generator; Real and fake data | |
| 505 | 8 | |a Random noiseDiscriminator and generator loss; GANs -- strengths and weaknesses; Summary; Section 2: Training GANs; Chapter 3: Implementing Your First GAN; Technical requirements; Imports; Implementing a Generator and Discriminator; Generator; Discriminator; Auxiliary functions; Training your GAN; Summary; Further reading; Chapter 4: Evaluating Your First GAN; The evaluation of GANs; Image quality; Image variety; Domain specifications; Qualitative methods; k-nearest neighbors; Mode analysis; Other methods; Quantitative methods; The Inception score; The Frechét Inception Distance | |
| 505 | 8 | |a Precision, Recall, and the F1 ScoreGANs and the birthday paradox; Summary; Chapter 5: Improving Your First GAN; Technical requirements; Challenges in training GANs; Mode collapse and mode drop; Training instability; Sensitivity to hyperparameter initialization; Vanishing gradients; Tricks of the trade; Tracking failure; Working with labels; Working with discrete inputs; Adding noise; Input normalization; Modified objective function; Distribute latent vector; Weight normalization; Avoid sparse gradients; Use a different optimizer; Learning rate schedule; GAN model architectures; ResNet GAN | |
| 505 | 8 | |a GAN algorithms and loss functionsLeast Squares GAN; Wasserstein GAN; Wasserstein GAN with gradient penalty; Relativistic GAN; Summary; Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio; Chapter 6: Synthesizing and Manipulating Images with GANs; Technical requirements; Image-to-image translation; Experimental setup; Data; Training; Imports; Training signature; Training setup; Training loop; Logging; pix2pix implementation; Custom layers; Discriminator; Generator; pix2pixHD implementation; Improvements to pix2pix; Custom layers; Discriminator; Generator | |
| 520 | |a This book will explore deep learning and generative models, and their applications in artificial intelligence. You will learn to evaluate and improve your GAN models by eliminating challenges that are encountered in real-world applications. You will implement GAN architectures in various domains such as computer vision, NLP, and audio processing. | ||
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| contents | Cover; Title Page; Copyright and Credits; About Packt; Foreword; Contributors; Table of Contents; Preface; Section 1: Introduction and Environment Setup; Chapter 1: Deep Learning Basics and Environment Setup; Deep learning basics; Artificial Neural Networks (ANNs); The parameter estimation; Backpropagation; Loss functions; L1 loss; L2 loss; Categorical crossentropy loss; Non-linearities; Sigmoid; Tanh; ReLU; A fully connected layer; The convolution layer; The max pooling layer; Deep learning environment setup; Installing Anaconda and Python; Setting up a virtual environment in Anaconda Installing TensorFlowInstalling Keras; Installing data visualization and machine learning libraries; The matplotlib library; The Jupyter library; The scikit-learn library; NVIDIA's CUDA Toolkit and cuDNN; The deep learning environment test; Summary; Chapter 2: Introduction to Generative Models; Discriminative and generative models compared; Comparing discriminative and generative models; Generative models; Autoregressive models; Variational autoencoders; Reversible flows; Generative adversarial networks; GANs -- building blocks; The discriminator; The generator; Real and fake data Random noiseDiscriminator and generator loss; GANs -- strengths and weaknesses; Summary; Section 2: Training GANs; Chapter 3: Implementing Your First GAN; Technical requirements; Imports; Implementing a Generator and Discriminator; Generator; Discriminator; Auxiliary functions; Training your GAN; Summary; Further reading; Chapter 4: Evaluating Your First GAN; The evaluation of GANs; Image quality; Image variety; Domain specifications; Qualitative methods; k-nearest neighbors; Mode analysis; Other methods; Quantitative methods; The Inception score; The Frechét Inception Distance Precision, Recall, and the F1 ScoreGANs and the birthday paradox; Summary; Chapter 5: Improving Your First GAN; Technical requirements; Challenges in training GANs; Mode collapse and mode drop; Training instability; Sensitivity to hyperparameter initialization; Vanishing gradients; Tricks of the trade; Tracking failure; Working with labels; Working with discrete inputs; Adding noise; Input normalization; Modified objective function; Distribute latent vector; Weight normalization; Avoid sparse gradients; Use a different optimizer; Learning rate schedule; GAN model architectures; ResNet GAN GAN algorithms and loss functionsLeast Squares GAN; Wasserstein GAN; Wasserstein GAN with gradient penalty; Relativistic GAN; Summary; Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio; Chapter 6: Synthesizing and Manipulating Images with GANs; Technical requirements; Image-to-image translation; Experimental setup; Data; Training; Imports; Training signature; Training setup; Training loop; Logging; pix2pix implementation; Custom layers; Discriminator; Generator; pix2pixHD implementation; Improvements to pix2pix; Custom layers; Discriminator; Generator |
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| publisher | Packt, |
| record_format | marc |
| spelling | Valle, Rafael, 1985- author. http://id.loc.gov/authorities/names/no2010009429 Hands-On Generative Adversarial Networks with Keras : Your Guide to Implementing Next-Generation Generative Adversarial Networks. Birmingham : Packt, 2019. 1 online resource (263 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from PDF title page (EBSCO, viewed August 30, 2019) Cover; Title Page; Copyright and Credits; About Packt; Foreword; Contributors; Table of Contents; Preface; Section 1: Introduction and Environment Setup; Chapter 1: Deep Learning Basics and Environment Setup; Deep learning basics; Artificial Neural Networks (ANNs); The parameter estimation; Backpropagation; Loss functions; L1 loss; L2 loss; Categorical crossentropy loss; Non-linearities; Sigmoid; Tanh; ReLU; A fully connected layer; The convolution layer; The max pooling layer; Deep learning environment setup; Installing Anaconda and Python; Setting up a virtual environment in Anaconda Installing TensorFlowInstalling Keras; Installing data visualization and machine learning libraries; The matplotlib library; The Jupyter library; The scikit-learn library; NVIDIA's CUDA Toolkit and cuDNN; The deep learning environment test; Summary; Chapter 2: Introduction to Generative Models; Discriminative and generative models compared; Comparing discriminative and generative models; Generative models; Autoregressive models; Variational autoencoders; Reversible flows; Generative adversarial networks; GANs -- building blocks; The discriminator; The generator; Real and fake data Random noiseDiscriminator and generator loss; GANs -- strengths and weaknesses; Summary; Section 2: Training GANs; Chapter 3: Implementing Your First GAN; Technical requirements; Imports; Implementing a Generator and Discriminator; Generator; Discriminator; Auxiliary functions; Training your GAN; Summary; Further reading; Chapter 4: Evaluating Your First GAN; The evaluation of GANs; Image quality; Image variety; Domain specifications; Qualitative methods; k-nearest neighbors; Mode analysis; Other methods; Quantitative methods; The Inception score; The Frechét Inception Distance Precision, Recall, and the F1 ScoreGANs and the birthday paradox; Summary; Chapter 5: Improving Your First GAN; Technical requirements; Challenges in training GANs; Mode collapse and mode drop; Training instability; Sensitivity to hyperparameter initialization; Vanishing gradients; Tricks of the trade; Tracking failure; Working with labels; Working with discrete inputs; Adding noise; Input normalization; Modified objective function; Distribute latent vector; Weight normalization; Avoid sparse gradients; Use a different optimizer; Learning rate schedule; GAN model architectures; ResNet GAN GAN algorithms and loss functionsLeast Squares GAN; Wasserstein GAN; Wasserstein GAN with gradient penalty; Relativistic GAN; Summary; Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio; Chapter 6: Synthesizing and Manipulating Images with GANs; Technical requirements; Image-to-image translation; Experimental setup; Data; Training; Imports; Training signature; Training setup; Training loop; Logging; pix2pix implementation; Custom layers; Discriminator; Generator; pix2pixHD implementation; Improvements to pix2pix; Custom layers; Discriminator; Generator This book will explore deep learning and generative models, and their applications in artificial intelligence. You will learn to evaluate and improve your GAN models by eliminating challenges that are encountered in real-world applications. You will implement GAN architectures in various domains such as computer vision, NLP, and audio processing. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. aat Computer modelling & simulation. bicssc Machine learning. bicssc Pattern recognition. bicssc Computer vision. bicssc Mathematical theory of computation. bicssc Computers Computer Vision & Pattern Recognition. bisacsh Computers Computer Simulation. bisacsh Computers Machine Theory. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Electronic book. has work: Hands-On Generative Adversarial Networks with Keras (Text) https://id.oclc.org/worldcat/entity/E39PCFQvTgQ7VkcYQdqfjqR8BX https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2117001 Volltext |
| spellingShingle | Valle, Rafael, 1985- Hands-On Generative Adversarial Networks with Keras : Your Guide to Implementing Next-Generation Generative Adversarial Networks. Cover; Title Page; Copyright and Credits; About Packt; Foreword; Contributors; Table of Contents; Preface; Section 1: Introduction and Environment Setup; Chapter 1: Deep Learning Basics and Environment Setup; Deep learning basics; Artificial Neural Networks (ANNs); The parameter estimation; Backpropagation; Loss functions; L1 loss; L2 loss; Categorical crossentropy loss; Non-linearities; Sigmoid; Tanh; ReLU; A fully connected layer; The convolution layer; The max pooling layer; Deep learning environment setup; Installing Anaconda and Python; Setting up a virtual environment in Anaconda Installing TensorFlowInstalling Keras; Installing data visualization and machine learning libraries; The matplotlib library; The Jupyter library; The scikit-learn library; NVIDIA's CUDA Toolkit and cuDNN; The deep learning environment test; Summary; Chapter 2: Introduction to Generative Models; Discriminative and generative models compared; Comparing discriminative and generative models; Generative models; Autoregressive models; Variational autoencoders; Reversible flows; Generative adversarial networks; GANs -- building blocks; The discriminator; The generator; Real and fake data Random noiseDiscriminator and generator loss; GANs -- strengths and weaknesses; Summary; Section 2: Training GANs; Chapter 3: Implementing Your First GAN; Technical requirements; Imports; Implementing a Generator and Discriminator; Generator; Discriminator; Auxiliary functions; Training your GAN; Summary; Further reading; Chapter 4: Evaluating Your First GAN; The evaluation of GANs; Image quality; Image variety; Domain specifications; Qualitative methods; k-nearest neighbors; Mode analysis; Other methods; Quantitative methods; The Inception score; The Frechét Inception Distance Precision, Recall, and the F1 ScoreGANs and the birthday paradox; Summary; Chapter 5: Improving Your First GAN; Technical requirements; Challenges in training GANs; Mode collapse and mode drop; Training instability; Sensitivity to hyperparameter initialization; Vanishing gradients; Tricks of the trade; Tracking failure; Working with labels; Working with discrete inputs; Adding noise; Input normalization; Modified objective function; Distribute latent vector; Weight normalization; Avoid sparse gradients; Use a different optimizer; Learning rate schedule; GAN model architectures; ResNet GAN GAN algorithms and loss functionsLeast Squares GAN; Wasserstein GAN; Wasserstein GAN with gradient penalty; Relativistic GAN; Summary; Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio; Chapter 6: Synthesizing and Manipulating Images with GANs; Technical requirements; Image-to-image translation; Experimental setup; Data; Training; Imports; Training signature; Training setup; Training loop; Logging; pix2pix implementation; Custom layers; Discriminator; Generator; pix2pixHD implementation; Improvements to pix2pix; Custom layers; Discriminator; Generator Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. aat Computer modelling & simulation. bicssc Machine learning. bicssc Pattern recognition. bicssc Computer vision. bicssc Mathematical theory of computation. bicssc Computers Computer Vision & Pattern Recognition. bisacsh Computers Computer Simulation. bisacsh Computers Machine Theory. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Hands-On Generative Adversarial Networks with Keras : Your Guide to Implementing Next-Generation Generative Adversarial Networks. |
| title_auth | Hands-On Generative Adversarial Networks with Keras : Your Guide to Implementing Next-Generation Generative Adversarial Networks. |
| title_exact_search | Hands-On Generative Adversarial Networks with Keras : Your Guide to Implementing Next-Generation Generative Adversarial Networks. |
| title_full | Hands-On Generative Adversarial Networks with Keras : Your Guide to Implementing Next-Generation Generative Adversarial Networks. |
| title_fullStr | Hands-On Generative Adversarial Networks with Keras : Your Guide to Implementing Next-Generation Generative Adversarial Networks. |
| title_full_unstemmed | Hands-On Generative Adversarial Networks with Keras : Your Guide to Implementing Next-Generation Generative Adversarial Networks. |
| title_short | Hands-On Generative Adversarial Networks with Keras : |
| title_sort | hands on generative adversarial networks with keras your guide to implementing next generation generative adversarial networks |
| title_sub | Your Guide to Implementing Next-Generation Generative Adversarial Networks. |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. aat Computer modelling & simulation. bicssc Machine learning. bicssc Pattern recognition. bicssc Computer vision. bicssc Mathematical theory of computation. bicssc Computers Computer Vision & Pattern Recognition. bisacsh Computers Computer Simulation. bisacsh Computers Machine Theory. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast |
| topic_facet | Machine learning. Neural networks (Computer science) Artificial intelligence. Neural Networks, Computer Artificial Intelligence Machine Learning Apprentissage automatique. Réseaux neuronaux (Informatique) Intelligence artificielle. artificial intelligence. Computer modelling & simulation. Pattern recognition. Computer vision. Mathematical theory of computation. Computers Computer Vision & Pattern Recognition. Computers Computer Simulation. Computers Machine Theory. Artificial intelligence Machine learning Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2117001 |
| work_keys_str_mv | AT vallerafael handsongenerativeadversarialnetworkswithkerasyourguidetoimplementingnextgenerationgenerativeadversarialnetworks |