Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music /

Packed with intriguing real-world projects as well as theory, Generative AI with Python and TensorFlow 2 enables you to leverage artificial intelligence creatively and generate human-like data in the form of speech, text, images, and music.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Babcock, Joseph (Autor)
Otros Autores: Bali, Raghav
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing, Limited, 2021.
Temas:
Artificial intelligence.
Pattern perception.
Computer vision.
Intelligence artificielle.
Perception des structures.
Vision par ordinateur.
artificial intelligence.
Neural networks & fuzzy systems.
Pattern recognition.
Computers > Intelligence (AI) & Semantics.
Computers > Computer Vision & Pattern Recognition.
Computers > Neural Networks.
Artificial intelligence
Computer vision
Pattern perception
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Home
  • Copyright
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: An Introduction to Generative AI: "Drawing" Data from Models
  • Applications of AI
  • Discriminative and generative models
  • Implementing generative models
  • The rules of probability
  • Discriminative and generative modeling and Bayes' theorem
  • Why use generative models?
  • The promise of deep learning
  • Building a better digit classifier
  • Generating images
  • Style transfer and image transformation
  • Fake news and chatbots
  • Sound composition
  • The rules of the game
  • Unique challenges of generative models
  • Summary
  • References
  • Chapter 2: Setting Up a TensorFlow Lab
  • Deep neural network development and TensorFlow
  • TensorFlow 2.0
  • VSCode
  • Docker: A lightweight virtualization solution
  • Important Docker commands and syntax
  • Connecting Docker containers with docker-compose
  • Kubernetes: Robust management of multi-container applications
  • Important Kubernetes commands
  • Kustomize for configuration management
  • Kubeflow: an end-to-end machine learning lab
  • Running Kubeflow locally with MiniKF
  • Installing Kubeflow in AWS
  • Installing Kubeflow in GCP
  • Installing Kubeflow on Azure
  • Installing Kubeflow using Terraform
  • A brief tour of Kubeflow's components
  • Kubeflow notebook servers
  • Kubeflow pipelines
  • Using Kubeflow Katib to optimize model hyperparameters
  • Summary
  • References
  • Chapter 3: Building Blocks of Deep Neural Networks
  • Perceptrons
  • a brain in a function
  • From tissues to TLUs
  • From TLUs to tuning perceptrons
  • Multi-layer perceptrons and backpropagation
  • Backpropagation in practice
  • The shortfalls of backpropagation
  • Varieties of networks: Convolution and recursive
  • Networks for seeing: Convolutional architectures
  • Early CNNs
  • AlexNet and other CNN innovations
  • AlexNet architecture.
  • Networks for sequence data
  • RNNs and LSTMs
  • Building a better optimizer
  • Gradient descent to ADAM
  • Xavier initialization
  • Summary
  • References
  • Chapter 4: Teaching Networks to Generate Digits
  • The MNIST database
  • Retrieving and loading the MNIST dataset in TensorFlow
  • Restricted Boltzmann Machines: generating pixels with statistical mechanics
  • Hopfield networks and energy equations for neural networks
  • Modeling data with uncertainty with Restricted Boltzmann Machines
  • Contrastive divergence: Approximating a gradient
  • Stacking Restricted Boltzmann Machines to generate images: the Deep Belief Network
  • Creating an RBM using the TensorFlow Keras layers API
  • Creating a DBN with the Keras Model API
  • Summary
  • References
  • Chapter 5: Painting Pictures with Neural Networks Using VAEs
  • Creating separable encodings of images
  • The variational objective
  • The reparameterization trick
  • Inverse Autoregressive Flow
  • Importing CIFAR
  • Creating the network from TensorFlow 2
  • Summary
  • References
  • Chapter 6: Image Generation with GANs
  • The taxonomy of generative models
  • Generative adversarial networks
  • The generator model
  • Training GANs
  • Non-saturating generator cost
  • Maximum likelihood game
  • Vanilla GAN
  • Improved GANs
  • Deep Convolutional GAN
  • Vector arithmetic
  • Conditional GAN
  • Wasserstein GAN
  • Progressive GAN
  • The overall method
  • Progressive growth-smooth fade-in
  • Minibatch standard deviation
  • Equalized learning rate
  • Pixelwise normalization
  • TensorFlow Hub implementation
  • Challenges
  • Training instability
  • Mode collapse
  • Uninformative loss and evaluation metrics
  • Summary
  • References
  • Chapter 7: Style Transfer with GANs
  • Paired style transfer using pix2pix GAN
  • The U-Net generator
  • The Patch-GAN discriminator
  • Loss
  • Training pix2pix
  • Use cases.
  • Unpaired style transfer using CycleGAN
  • Overall setup for CycleGAN
  • Adversarial loss
  • Cycle loss
  • Identity loss
  • Overall loss
  • Hands-on: Unpaired style transfer with CycleGAN
  • Generator setup
  • Discriminator setup
  • GAN setup
  • The training loop
  • Related works
  • DiscoGAN
  • DualGAN
  • Summary
  • References
  • Chapter 8: Deepfakes with GANs
  • Deepfakes overview
  • Modes of operation
  • Replacement
  • Re-enactment
  • Editing
  • Key feature set
  • Facial Action Coding System (FACS)
  • 3D Morphable Model
  • Facial landmarks
  • Facial landmark detection using OpenCV
  • Facial landmark detection using dlib
  • Facial landmark detection using MTCNN
  • High-level workflow
  • Common architectures
  • Encoder-Decoder (ED)
  • Generative Adversarial Networks (GANs)
  • Replacement using autoencoders
  • Task definition
  • Dataset preparation
  • Autoencoder architecture
  • Training our own face swapper
  • Results and limitations
  • Re-enactment using pix2pix
  • Dataset preparation
  • Pix2pix GAN setup and training
  • Results and limitations
  • Challenges
  • Ethical issues
  • Technical challenges
  • Generalization
  • Occlusions
  • Temporal issues
  • Off-the-shelf implementations
  • Summary
  • References
  • Chapter 9: The Rise of Methods for Text Generation
  • Representing text
  • Bag of Words
  • Distributed representation
  • Word2vec
  • GloVe
  • FastText
  • Text generation and the magic of LSTMs
  • Language modeling
  • Hands-on: Character-level language model
  • Decoding strategies
  • Greedy decoding
  • Beam search
  • Sampling
  • Hands-on: Decoding strategies
  • LSTM variants and convolutions for text
  • Stacked LSTMs
  • Bidirectional LSTMs
  • Convolutions and text
  • Summary
  • References
  • Chapter 10: NLP 2.0: Using Transformers to Generate Text
  • Attention
  • Contextual embeddings
  • Self-attention
  • Transformers
  • Overall architecture.
  • Multi-head self-attention
  • Positional encodings
  • BERT-ology
  • GPT 1, 2, 3 ...
  • Generative pre-training: GPT
  • GPT-2
  • Hands-on with GPT-2
  • Mammoth GPT-3
  • Summary
  • References
  • Chapter 11: Composing Music with Generative Models
  • Getting started with music generation
  • Representing music
  • Music generation using LSTMs
  • Dataset preparation
  • LSTM model for music generation
  • Music generation using GANs
  • Generator network
  • Discriminator network
  • Training and results
  • MuseGAN
  • polyphonic music generation
  • Jamming model
  • Composer model
  • Hybrid model
  • Temporal model
  • MuseGAN
  • Generators
  • Critic
  • Training and results
  • Summary
  • References
  • Chapter 12: Play Video Games with Generative AI: GAIL
  • Reinforcement learning: Actions, agents, spaces, policies, and rewards
  • Deep Q-learning
  • Inverse reinforcement learning: Learning from experts
  • Adversarial learning and imitation
  • Running GAIL on PyBullet Gym
  • The agent: Actor-Critic network
  • The discriminator
  • Training and results
  • Summary
  • References
  • Chapter 13: Emerging Applications in Generative AI
  • Introduction
  • Finding new drugs with generative models
  • Searching chemical space with generative molecular graph networks
  • Folding proteins with generative models
  • Solving partial differential equations with generative modeling
  • Few shot learning for creating videos from images
  • Generating recipes with deep learning
  • Summary
  • References
  • Other Books You May Enjoy
  • Index.

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