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Generative Adversarial Networks for Image Generation

that had been generated by a GAN for $432,000. Although image generation has been challenging, GAN image generation has proved to be;

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Generative Adversarial Networks for Image-to-Image Translation

in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative;

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Generative Deep Learning Teaching Machines to Paint, Write, Compose and Play

practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for;

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Hands-On Image Generation with TensorFlow

Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch;

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Handbook of Decision Support Systems for Neurological Disorders

Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided;

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GANs in Action

images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a;

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Practical TensorFlow.js

browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser;

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Practical TensorFlow.js

browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser;

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Generative Adversarial Networks Cookbook

Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras Key Features;

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Reinforcement Learning for Finance

library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN - two;

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Practical Convolutional Neural Networks

object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models;

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Generative AI with Python and TensorFlow 2

easy for PyTorch users to follow along Look inside the most famous deep generative models, from GPT to MuseGAN Learn to build and adapt your;

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Simulation and Synthesis in Medical Imaging

generative models or adversarial learning for MRI/CT/ microscopy image synthesis, and several applications of image synthesis and simulation for data;

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Simulation and Synthesis in Medical Imaging

generative models or adversarial learning for MRI/CT/PET/microscopy image synthesis, image super resolution, and several applications of image;

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Medical Image Computing and Computer Assisted Intervention MICCAI 2020

learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase;

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Medical Image Computing and Computer Assisted Intervention MICCAI 2020

; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and;

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Medical Image Computing and Computer Assisted Intervention MICCAI 2020

; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and;

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TensorFlow Deep Learning Projects

environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock;

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Simulation and Synthesis in Medical Imaging: 5th International Workshop, Sashimi 2020, Held in Conjunction with Miccai 2020, Lima, Peru, October 4, 20

generative models or adversarial learning for MRI/CT/PET/microscopy image synthesis, and several applications of image synthesis and simulation for;

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Strengthening Deep Neural Networks Making AI Less Susceptible to Adversarial Trickery

this adversarial input, and methods for increasing AI robustness to these attacks. If you're a data scientist developing DNN algorithms, a;

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Hands-On Convolutional Neural Networks with TensorFlow

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for;

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Python Deep Learning

in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current;

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Applied Deep Learning with TensorFlow 2

fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This;

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Applied Deep Learning with TensorFlow 2

fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book;

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Machine Learning for Computer Scientists and Data Analysts

, through complex neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book;

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Advanced Deep Learning with R

Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key;

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Introduction to Deep Learning Business Applications for Developers

modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks;

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