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.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2021.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (489 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 1800208502 9781800208506 |
Internformat
MARC
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| 245 | 1 | 0 | |a Generative AI with Python and TensorFlow 2 : |b harness the power of generative models to create images, text, and music / |c Joseph Babcock, Raghav Bali. |
| 264 | 1 | |a Birmingham : |b Packt Publishing, Limited, |c 2021. | |
| 300 | |a 1 online resource (489 pages) | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 630 | 0 | 7 | |a TensorFlow. |2 lemac |
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| 650 | 0 | |a Computer vision. |0 http://id.loc.gov/authorities/subjects/sh85029549 | |
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| 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. 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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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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="630" ind1="0" ind2="7"><subfield code="a">TensorFlow.</subfield><subfield code="2">lemac</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85008180</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Pattern perception.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85098789</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Computer vision.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85029549</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Intelligence artificielle.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Perception des structures.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield 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| id | ZDB-4-EBA-on1250079404 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:17Z |
| institution | BVB |
| isbn | 1800208502 9781800208506 |
| language | English |
| oclc_num | 1250079404 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (489 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Packt Publishing, Limited, |
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| spelling | Babcock, Joseph, author. Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music / Joseph Babcock, Raghav Bali. Birmingham : Packt Publishing, Limited, 2021. 1 online resource (489 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from PDF title page (viewed Janurary 3, 2022). Includes bibliographical references and index. 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. 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. TensorFlow. lemac Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Pattern perception. http://id.loc.gov/authorities/subjects/sh85098789 Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Intelligence artificielle. Perception des structures. Vision par ordinateur. artificial intelligence. aat Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Pattern recognition. bicssc Computer vision. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Computer Vision & Pattern Recognition. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Computer vision fast Pattern perception fast Intel·ligència artificial. lemac Python (Llenguatge de programació) lemac Bali, Raghav. has work: GENERATIVE AI WITH PYTHON AND TENSORFLOW 2 (Text) https://id.oclc.org/worldcat/entity/E39PCYKQCVWW9VDY3gD9P4x343 https://id.oclc.org/worldcat/ontology/hasWork Print version: Babcock, Joseph. Generative AI with Python and TensorFlow 2. Birmingham : Packt Publishing, Limited, ©2021 9781800200883 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2923092 Volltext |
| spellingShingle | Babcock, Joseph Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music / 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. TensorFlow. lemac Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Pattern perception. http://id.loc.gov/authorities/subjects/sh85098789 Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Intelligence artificielle. Perception des structures. Vision par ordinateur. artificial intelligence. aat Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Pattern recognition. bicssc Computer vision. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Computer Vision & Pattern Recognition. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Computer vision fast Pattern perception fast Intel·ligència artificial. lemac Python (Llenguatge de programació) lemac |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh85098789 http://id.loc.gov/authorities/subjects/sh85029549 |
| title | Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music / |
| title_auth | Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music / |
| title_exact_search | Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music / |
| title_full | Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music / Joseph Babcock, Raghav Bali. |
| title_fullStr | Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music / Joseph Babcock, Raghav Bali. |
| title_full_unstemmed | Generative AI with Python and TensorFlow 2 : harness the power of generative models to create images, text, and music / Joseph Babcock, Raghav Bali. |
| title_short | Generative AI with Python and TensorFlow 2 : |
| title_sort | generative ai with python and tensorflow 2 harness the power of generative models to create images text and music |
| title_sub | harness the power of generative models to create images, text, and music / |
| topic | TensorFlow. lemac Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Pattern perception. http://id.loc.gov/authorities/subjects/sh85098789 Computer vision. http://id.loc.gov/authorities/subjects/sh85029549 Intelligence artificielle. Perception des structures. Vision par ordinateur. artificial intelligence. aat Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Pattern recognition. bicssc Computer vision. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Computer Vision & Pattern Recognition. bisacsh Computers Neural Networks. bisacsh Artificial intelligence fast Computer vision fast Pattern perception fast Intel·ligència artificial. lemac Python (Llenguatge de programació) lemac |
| topic_facet | TensorFlow. 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 Intel·ligència artificial. Python (Llenguatge de programació) |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2923092 |
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