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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.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2021.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Babcock, Joseph, |e author. | |
| 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) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (viewed Janurary 3, 2022). | |
| 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. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Pattern perception. | |
| 650 | 0 | |a Computer vision. | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Perception des structures. | |
| 650 | 6 | |a Vision par ordinateur. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Pattern recognition. |2 bicssc | |
| 650 | 7 | |a Computer vision. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Computer Vision & Pattern Recognition. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Computer vision |2 fast | |
| 650 | 7 | |a Pattern perception |2 fast | |
| 700 | 1 | |a Bali, Raghav. | |
| 776 | 0 | 8 | |i Print version: |a Babcock, Joseph. |t Generative AI with Python and TensorFlow 2. |d Birmingham : Packt Publishing, Limited, ©2021 |z 9781800200883 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781800200883/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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