Hands-On Generative Adversarial Networks with Keras

Valle, Rafael

  • 出版商: Packt Publishing
  • 出版日期: 2019-05-03
  • 售價: $1,680
  • 貴賓價: 9.5$1,596
  • 語言: 英文
  • 頁數: 272
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789538203
  • ISBN-13: 9781789538205
  • 相關分類: DeepLearning
  • 立即出貨 (庫存=1)

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商品描述

Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step towards understanding GAN architectures and tackling the challenges involved in training them.

This book opens with an introduction to deep learning and generative models, and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that give you the ability to control characteristics of GAN outputs. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN.

By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing.

Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA

商品描述(中文翻譯)

生成對抗網絡(GAN)已經在機器學習和深度學習領域引起了革命。本書將是您了解GAN架構並應對訓練中的挑戰的第一步。

本書首先介紹了深度學習和生成模型以及它們在人工智能(AI)中的應用。然後,您將通過易於理解的示例學習如何構建、評估和改進您的第一個GAN。接下來的幾章將指導您通過訓練GAN模型來生成和改進高分辨率圖像。您還將學習如何實現有條件的GAN,以控制GAN輸出的特徵。通過探索一種新的訓練方法,逐步增長GAN,您將進一步擴展您的知識。接著,您將深入了解使用GAN進行圖像合成、語音增強和自然語言生成的最新模型。此外,您還將能夠使用TequilaGAN識別GAN樣本。

通過閱讀本書,您將熟悉使用各種示例和數據集的GAN框架的最新進展,並具備實施GAN架構用於計算機視覺、自然語言處理(NLP)和音頻處理等多個任務和領域所需的技能。

前言由NVIDIA高級研究科學家Ting-Chun Wang撰寫。