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

Format:

Book

Author/Creator:

Babcock, Joseph, author.

Bali, Raghav, author.

Language:

English

Subjects (All):

Artificial intelligence.

Physical Description:

1 online resource (489 pages)

Other Title:

Generative AI with Python and TensorFlow two

Generative AI with Python and TensorFlow 2 : create images, text, and music with VAEs, GANs, LSTMs, transformer models

Place of Publication:

Birmingham, England ; Mumbai : Packt Publishing, April 2021

Summary:

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.

Contents:

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

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

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

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

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

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

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

Pix2pix GAN setup and training

Ethical issues

Technical challenges

Generalization

Occlusions

Temporal issues

Off-the-shelf implementations

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

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

Chapter 11: Composing Music with Generative Models

Getting started with music generation

Representing music

Music generation using LSTMs

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

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

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

Other Books You May Enjoy

Index.

Notes:

Description based on print version record.

ISBN:

9781800208506

1800208502

OCLC:

1250079404