| Description |
1 online resource (489 pages) |
| Bibliography |
Includes bibliographical references and index. |
| 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 -- 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. |
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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. |
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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. |
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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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Artificial intelligence.
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Pattern perception.
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Computer vision.
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artificial intelligence. |
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Artificial intelligence. |
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Neural networks & fuzzy systems. |
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Pattern recognition. |
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Computer vision. |
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Computers -- Intelligence (AI) & Semantics. |
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Computers -- Computer Vision & Pattern Recognition. |
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Computers -- Neural Networks. |
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Artificial intelligence |
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Computer vision |
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Pattern perception |
| Added Author |
Bali, Raghav.
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| Other Form: |
Print version: Babcock, Joseph. Generative AI with Python and TensorFlow 2. Birmingham : Packt Publishing, Limited, ©2021 9781800200883 |
| ISBN |
1800208502 (electronic book) |
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9781800208506 (electronic book) |
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1800200889 |
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9781800200883 |
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