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

《使用 TensorFlow 2 和 Keras 的深度學習（第二版）》教授神經網絡和深度學習技術，並結合 TensorFlow (TF) 和 Keras。您將學會如何在最強大、最受歡迎且可擴展的機器學習堆疊中編寫深度學習應用程序。

TensorFlow 是專業應用的首選機器學習庫，而 Keras 則提供了一個簡單而強大的 Python API 來訪問 TensorFlow。TensorFlow 2 提供了完整的 Keras 整合，使得高級機器學習比以往任何時候都更簡單和方便。

本書還介紹了使用 TensorFlow 的神經網絡，涵蓋主要應用（回歸、卷積神經網絡 (CNNs)、生成對抗網絡 (GANs)、遞歸神經網絡 (RNNs)、自然語言處理 (NLP)），並涵蓋兩個實作範例應用，然後深入探討 TensorFlow 在生產環境中的應用、TensorFlow 移動端以及如何使用 TensorFlow 與 AutoML。

- 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
- 通過清晰的解釋和廣泛的代碼範例理解深度學習和機器學習的基本原理

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對於建立和管理全球技術人才、創新和執行充滿熱情。他的核心專長在於雲端運算、深度學習和搜尋引擎。目前，他擔任Google Cloud首席技術官辦公室的工程總監。之前，他擔任Google華沙網站的負責人，將工程網站的規模擴大了一倍。

Amita Kapoor是德里大學SRCASW電子系的副教授，並在過去20年中積極教授神經網絡和人工智慧。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超過50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，該實驗室是Reed-Elsevier集團內的一個先進技術小組。他的興趣領域包括語義搜尋、自然語言處理、機器學習和深度學習。在Elsevier，他參與了多個與搜尋質量測量和改進、圖像分類和重複檢測、以及醫學和科學語料的註釋和本體開發相關的計畫。除了與Antonio Gulli共同撰寫一本關於深度學習的書籍外，Sujit還在他的部落格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)


  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

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