Deep Learning with TensorFlow 2 and Keras, 2/e (Paperback) (使用 TensorFlow 2 和 Keras 的深度學習（第二版）)

Gulli, Antonio, Pal, Sujit, Kapoor, Amita

Deep Learning with TensorFlow 2 and Keras, 2/e (Paperback)

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商品描述

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.

 

TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.

 

This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.

  • ntroduces and then uses TensorFlow 2 and Keras right from the start
  • Teaches key machine and deep learning techniques
  • Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples

商品描述(中文翻譯)

《深度學習與 TensorFlow 2 和 Keras 第二版》教授神經網絡和深度學習技術，並結合 TensorFlow（TF）和 Keras 進行教學。您將學習如何在最強大、最受歡迎和可擴展的機器學習框架中撰寫深度學習應用程式。

TensorFlow 是專業應用程式的機器學習庫首選，而 Keras 提供了一個簡單而強大的 Python API，用於訪問 TensorFlow。TensorFlow 2 提供了完整的 Keras 整合，使高級機器學習比以往更容易和便利。

本書還介紹了使用 TensorFlow 的神經網絡，涵蓋了主要應用領域（回歸、卷積神經網絡（CNN）、生成對抗網絡（GAN）、循環神經網絡（RNN）、自然語言處理（NLP）），並提供了兩個實例應用程式，然後深入探討了 TF 在生產環境中的應用、TF 移動版以及如何使用 TensorFlow 進行自動機器學習（AutoML）。

本書的特點包括：
- 從一開始就介紹並使用 TensorFlow 2 和 Keras
- 教授關鍵的機器學習和深度學習技術
- 通過清晰的解釋和大量的程式碼示例，理解深度學習和機器學習的基礎知識

作者簡介

Antonio Gulli has a passion for establishing and managing global technological talent, for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, he serves as the Engineering Director for the Office of the CTO, Google Cloud. Previously, he served as Google Warsaw Site leader doubling the size of the engineering site.

Amita Kapoor is an associate professor in the Department of Electronics, SRCASW, University of Delhi, and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her master's in electronics in 1996 and her PhD in 2011. She has more than 50 publications in international journals and conferences. Her present research areas include machine learning, artificial intelligence, deep reinforcement learning, and robotics.

Sujit Pal is a technology research director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group. His areas of interest include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora. In addition to co-authoring a book on deep learning with Antonio Gulli, Sujit writes about technology on his blog, Salmon Run.

作者簡介(中文翻譯)

Antonio Gulli 是一位對於建立和管理全球技術人才、創新和執行具有熱情的人。他的核心專業領域包括雲計算、深度學習和搜索引擎。目前，他擔任 Google Cloud 技術長辦公室的工程總監。之前，他曾擔任 Google Warsaw 站點負責人，將該工程站點的規模增加了一倍。

Amita Kapoor 是德里大學電子系的副教授，積極從事神經網絡和人工智慧的教學工作已有20年。她於1996年獲得電子學碩士學位，並於2011年獲得博士學位。她在國際期刊和會議上發表了50多篇論文。她目前的研究領域包括機器學習、人工智慧、深度強化學習和機器人技術。

Sujit Pal 是 Elsevier Labs 的技術研究總監，該機構是 Reed-Elsevier 集團內的一個先進技術團隊。他的興趣領域包括語義搜索、自然語言處理、機器學習和深度學習。在 Elsevier，他參與了多個項目，包括搜索質量測量和改進、圖像分類和重複檢測，以及醫學和科學文獻的註釋和本體論開發。除了與 Antonio Gulli 合著一本關於深度學習的書籍外，Sujit 還在他的博客 Salmon Run 上撰寫有關技術的文章。

目錄大綱

  1. Neural Network Foundations with TensorFlow 2.0
  2. TensorFlow 1.x and 2.x
  3. Regression
  4. Convolutional Neural Networks
  5. Advanced Convolutional Neural Networks
  6. Generative Adversarial Networks
  7. Word Embeddings
  8. Recurrent Neural Networks
  9. Autoencoders
  10. Unsupervised Learning
  11. Reinforcement Learning
  12. TensorFlow and Cloud
  13. TensorFlow for Mobile and IoT and TensorFlow.js
  14. An introduction to AutoML
  15. The Math Behind Deep Learning
  16. Tensor Processing Unit

目錄大綱(中文翻譯)

- Neural Network Foundations with TensorFlow 2.0
- TensorFlow 1.x 和 2.x
- 回歸
- 卷積神經網絡
- 進階卷積神經網絡
- 生成對抗網絡
- 詞嵌入
- 遞歸神經網絡
- 自編碼器
- 非監督學習
- 強化學習
- TensorFlow 和雲端
- TensorFlow 適用於行動裝置和物聯網以及 TensorFlow.js
- 自動機器學習介紹
- 深度學習背後的數學
- 張量處理單元

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