Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras
暫譯: 生成對抗網絡專案：使用 TensorFlow 和 Keras 建立下一代生成模型

Kailash Ahirwar

Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras

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商品描述

Explore various Generative Adversarial Network architectures using the Python ecosystem

Key Features

  • Use different datasets to build advanced projects in the Generative Adversarial Network domain
  • Implement projects ranging from generating 3D shapes to a face aging application
  • Explore the power of GANs to contribute in open source research and projects

Book Description

Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain.

Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you'll gain an understanding of the architecture and functioning of generative models through their practical implementation.

By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.

What you will learn

  • Train a network on the 3D ShapeNet dataset to generate realistic shapes
  • Generate anime characters using the Keras implementation of DCGAN
  • Implement an SRGAN network to generate high-resolution images
  • Train Age-cGAN on Wiki-Cropped images to improve face verification
  • Use Conditional GANs for image-to-image translation
  • Understand the generator and discriminator implementations of StackGAN in Keras

Who this book is for

If you're a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.

Table of Contents

  1. Introduction to Generative Adversarial Networks
  2. 3D-GAN - Generating Shapes Using GANs
  3. Face Aging Using Conditional GAN
  4. Generating Anime Characters Using DCGANs
  5. Using SRGANs to Generate Photo-Realistic Images
  6. StackGAN- Text to Photo-Realistic Image Synthesis
  7. CycleGAN- Turn Paintings into Photos
  8. Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks
  9. Predicting the Future of GANs

商品描述(中文翻譯)

**探索使用 Python 生態系統的各種生成對抗網路架構**

#### 主要特點
- 使用不同的數據集來構建生成對抗網路領域的高級項目
- 實現從生成 3D 形狀到面部老化應用的項目
- 探索 GAN 的力量，以貢獻於開源研究和項目

#### 書籍描述
生成對抗網路（Generative Adversarial Networks, GANs）具有構建下一代模型的潛力，因為它們可以模擬任何數據分佈。由於這是機器學習中快速增長的領域之一，該領域正在進行大量的研究和開發工作。本書將測試無監督技術以訓練神經網路，並在 GAN 領域構建七個端到端的項目。

《生成對抗網路項目》首先涵蓋您將用來構建高效項目的概念、工具和庫。您還將使用各種數據集來進行書中涵蓋的不同項目。所需操作的複雜性隨著每一章的增加而增加，幫助您掌握使用 GAN 的技巧。您將涵蓋流行的方法，如 3D-GAN、DCGAN、StackGAN 和 CycleGAN，並通過實際實現來理解生成模型的架構和運作。

在本書結束時，您將準備好在工作或自己的項目中構建、訓練和優化自己的端到端 GAN 模型。

#### 您將學到的內容
- 在 3D ShapeNet 數據集上訓練網路以生成真實的形狀
- 使用 DCGAN 的 Keras 實現生成動漫角色
- 實現 SRGAN 網路以生成高解析度圖像
- 在 Wiki-Cropped 圖像上訓練 Age-cGAN 以改善面部驗證
- 使用條件 GAN 進行圖像到圖像的轉換
- 理解 Keras 中 StackGAN 的生成器和判別器實現

#### 本書適合誰
如果您是數據科學家、機器學習開發者、深度學習實踐者或 AI 愛好者，並尋找一個項目指南來測試您在構建實際 GAN 模型方面的知識和專業技能，那麼這本書適合您。

#### 目錄
1. 生成對抗網路簡介
2. 3D-GAN - 使用 GAN 生成形狀
3. 使用條件 GAN 進行面部老化
4. 使用 DCGAN 生成動漫角色
5. 使用 SRGAN 生成照片真實的圖像
6. StackGAN - 文本到照片真實圖像合成
7. CycleGAN - 將畫作轉換為照片
8. 條件 GAN - 使用條件對抗網路進行圖像到圖像的轉換
9. 預測 GAN 的未來

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