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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 1 | 0 | |a Hands-On Generative Adversarial Networks with Keras : |b Your Guide to Implementing Next-Generation Generative Adversarial Networks. |
| 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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