Generative deep learning : teaching machines to paint, write, compose, and play

Generative deep learning teaching machines to paint, write, compose, and play

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Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs),...

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Detalles Bibliográficos
Otros Autores: Foster, David (Business consultant), author (author), Friston, Karl, writer of foreword (writer of foreword)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, Incorporated 2023.
Edición:Second edition
Materias:
Machine learning.
Artificial intelligence.
Neural networks (Computer science)
Generative programming (Computer science)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009739030906719
Tabla de Contenidos:
  • Part I. Introduction to Generative Deep Learning
  • Chapter 1. Generative Modeling
  • What Is Generative Modeling?
  • Generative Versus Discriminative Modeling
  • The Rise of Generative Modeling
  • Generative Modeling and AI
  • Our First Generative Model
  • Hello World!
  • The Generative Modeling Framework
  • Representation Learning
  • Core Probability Theory
  • Generative Model Taxonomy
  • The Generative Deep Learning Codebase
  • Cloning the Repository
  • Using Docker
  • Running on a GPU
  • Summary
  • Chapter 2. Deep Learning
  • Data for Deep Learning
  • Deep Neural Networks
  • What Is a Neural Network?
  • Learning High-Level Features
  • TensorFlow and Keras
  • Multilayer Perceptron (MLP)
  • Preparing the Data
  • Building the Model
  • Compiling the Model
  • Training the Model
  • Evaluating the Model
  • Convolutional Neural Network (CNN)
  • Convolutional Layers
  • Batch Normalization
  • Dropout
  • Building the CNN
  • Training and Evaluating the CNN
  • Summary
  • Part II. Methods
  • Chapter 3. Variational Autoencoders
  • Introduction
  • Autoencoders
  • The Fashion-MNIST Dataset
  • The Autoencoder Architecture
  • The Encoder
  • The Decoder
  • Joining the Encoder to the Decoder
  • Reconstructing Images
  • Visualizing the Latent Space
  • Generating New Images
  • Variational Autoencoders
  • The Encoder
  • The Loss Function
  • Training the Variational Autoencoder
  • Analysis of the Variational Autoencoder
  • Exploring the Latent Space
  • The CelebA Dataset
  • Training the Variational Autoencoder
  • Analysis of the Variational Autoencoder
  • Generating New Faces
  • Latent Space Arithmetic
  • Morphing Between Faces
  • Summary
  • Chapter 4. Generative Adversarial Networks
  • Introduction
  • Deep Convolutional GAN (DCGAN)
  • The Bricks Dataset
  • The Discriminator
  • The Generator
  • Training the DCGAN
  • Analysis of the DCGAN
  • GAN Training: Tips and Tricks
  • Wasserstein GAN with Gradient Penalty (WGAN-GP)
  • Wasserstein Loss
  • The Lipschitz Constraint
  • Enforcing the Lipschitz Constraint
  • The Gradient Penalty Loss
  • Training the WGAN-GP
  • Analysis of the WGAN-GP
  • Conditional GAN (CGAN)
  • CGAN Architecture
  • Training the CGAN
  • Analysis of the CGAN
  • Summary
  • Chapter 5. Autoregressive Models
  • Introduction
  • Long Short-Term Memory Network (LSTM)
  • The Recipes Dataset
  • Working with Text Data
  • Tokenization
  • Creating the Training Set
  • The LSTM Architecture
  • The Embedding Layer
  • The LSTM Layer
  • The LSTM Cell
  • Training the LSTM
  • Analysis of the LSTM
  • Recurrent Neural Network (RNN) Extensions
  • Stacked Recurrent Networks
  • Gated Recurrent Units
  • Bidirectional Cells
  • PixelCNN

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