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

Description

Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you'll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You'll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There's been an explosion in potential use cases for generative models. You'll look at Open AI's news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that's under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning. Table of ContentsAn Introduction to Generative AI: "Drawing" Data from ModelsSetting Up a TensorFlow LabBuilding Blocks of Deep Neural NetworksTeaching Networks to Generate DigitsPainting Pictures with Neural Networks Using VAEsImage Generation with GANsStyle Transfer with GANsDeepfakes with GANsThe Rise of Methods for Text GenerationNLP 2.0: Using Transformers to Generate TextComposing Music with Generative ModelsPlay Video Games with Generative AI: GAILEmerging Applications in Generative AI 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