Generative AI with Python and TensorFlow 2: Harness the power of generative models to create images, text, and music
暫譯: 使用 Python 和 TensorFlow 2 的生成式 AI：利用生成模型的力量創造圖像、文本和音樂

Babcock, Joseph, Bali, Raghav

Generative AI with Python and TensorFlow 2: Harness the power of generative models to create images, text, and music

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商品描述

Fun and exciting projects to learn what artificial minds can create


Key Features:

  • Code examples are in TensorFlow 2, which make it 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 own models in TensorFlow 2.x
  • Explore exciting, cutting-edge use cases for deep generative AI


Book Description:

Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?


In this book, you'll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You'll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.


There's been an explosion in potential use cases for generative models. You'll look at Open AI's news generator, deepfakes, and training deep learning agents to navigate a simulated environment.


Recreate the code that's under the hood and uncover surprising links between text, image, and music generation.


What You Will Learn:

  • Export the code from GitHub into Google Colab to see how everything works for yourself
  • Compose music using LSTM models, simple GANs, and MuseGAN
  • Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN
  • Learn how attention and transformers have changed NLP
  • Build several text generation pipelines based on LSTMs, BERT, and GPT-2
  • Implement paired and unpaired style transfer with networks like StyleGAN
  • Discover emerging applications of generative AI like folding proteins and creating videos from images


Who this book is for:

This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

商品描述(中文翻譯)

**有趣且令人興奮的專案，學習人工智慧心智能創造的事物**

**主要特點：**
- 範例程式碼使用 TensorFlow 2，讓 PyTorch 使用者也能輕鬆跟上
- 深入了解最著名的深度生成模型，從 GPT 到 MuseGAN
- 學習如何在 TensorFlow 2.x 中建立和調整自己的模型
- 探索深度生成 AI 的令人興奮的前沿應用案例

**書籍描述：**
機器在創造性人類技能方面表現出色，例如繪畫、寫作和作曲。你能比生成 AI 更具創造力嗎？

在這本書中，你將探索生成模型的演變，從限制玻爾茲曼機和深度信念網絡到變分自編碼器（VAEs）和生成對抗網絡（GANs）。你將學習如何在 TensorFlow 中實現模型，並掌握最新的深度神經網絡研究。

生成模型的潛在應用案例激增。你將研究 Open AI 的新聞生成器、深度偽造技術，以及訓練深度學習代理在模擬環境中導航。

重建底層的程式碼，並揭示文本、圖像和音樂生成之間的驚人聯繫。

**你將學到的內容：**
- 從 GitHub 將程式碼匯出到 Google Colab，親自查看一切如何運作
- 使用 LSTM 模型、簡單的 GAN 和 MuseGAN 創作音樂
- 使用面部特徵點、自編碼器和 pix2pix GAN 創建深度偽造
- 學習注意力機制和變壓器如何改變自然語言處理（NLP）
- 基於 LSTMs、BERT 和 GPT-2 建立多個文本生成管道
- 使用像 StyleGAN 的網絡實現配對和非配對的風格轉換
- 發現生成 AI 的新興應用，如摺疊蛋白質和從圖像創建視頻

**本書適合誰：**
這是一本針對熱衷於創作並希望使用生成模型來獲得樂趣的 Python 程式設計師的書。為了充分利用這本書，你應該對機器學習的數學和統計有基本的了解。

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