Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection
暫譯: 進階應用深度學習：卷積神經網絡與物體檢測

Michelucci, Umberto

Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection

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商品描述

Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow.

Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models.

 

Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.

 

 

What You Will Learn

 

 

  • See how convolutional neural networks and object detection work
  • Save weights and models on disk
  • Pause training and restart it at a later stage
  • Use hardware acceleration (GPUs) in your code
  • Work with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning
  • Remove and add layers to pre-trained networks to adapt them to your specific project
  • Apply pre-trained models such as Alexnet and VGG16 to new datasets

 

 

Who This Book Is For

Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.

 

 

商品描述(中文翻譯)

開發和優化具有先進架構的深度學習模型。本書教您卷積神經網絡核心算法的複雜細節和微妙之處。在《進階應用深度學習》中，您將學習使用 Keras 和 TensorFlow 進行 CNN 和物體檢測的進階主題。

在此過程中，您將了解 CNN 中的基本操作，例如卷積和池化，然後研究更先進的架構，如 inception networks、resnets 等等。雖然本書討論理論主題，但您將發現如何有效地使用 Keras，並學習許多技巧和提示，包括如何使用自定義回調類來自定義 Keras 的日誌記錄、什麼是 eager execution 以及如何在您的模型中使用它。

最後，您將學習物體檢測的工作原理，並在 Keras 和 TensorFlow 中構建 YOLO（You Only Look Once）算法的完整實現。到本書結束時，您將在 Keras 中實現各種模型，並學習許多進階技巧，將您的技能提升到一個新水平。

您將學到的內容：

- 了解卷積神經網絡和物體檢測的工作原理
- 將權重和模型保存到磁碟
- 暫停訓練並在稍後階段重新啟動
- 在您的代碼中使用硬體加速（GPUs）
- 使用 Dataset TensorFlow 抽象，並使用預訓練模型和遷移學習
- 移除和添加層到預訓練網絡，以使其適應您的特定項目
- 將預訓練模型如 Alexnet 和 VGG16 應用於新數據集

本書適合對象：

具備中級至高級 Python 和機器學習知識的科學家和研究人員。此外，預期具備 Keras 和 TensorFlow 的中級知識。

作者簡介

Umberto Michelucci studied physics and mathematics. He is an expert in numerical simulation, statistics, data science, and machine learning. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His last book Applied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks was published by Apress in 2018. He is very active in research in the field of artificial intelligence and publishes his research results regularly in leading journals and gives regular talks at international conferences.
He teaches as a lecturer at the Zurich University of Applied Sciences and at the HWZ University of Applied Sciences in Business Administration. He is also responsible for AI, research, and new technologies at Helsana Vesicherung AG.
He recently founded TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for AI, to make AI technologies and research accessible to everyone.

作者簡介(中文翻譯)

翁貝托·米切魯奇（Umberto Michelucci）研究物理學和數學。他是數值模擬、統計學、數據科學和機器學習的專家。除了在喬治·華盛頓大學（George Washington University, USA）和奧格斯堡大學（University of Augsburg, DE）擁有數年的研究經驗外，他在數據倉庫、數據科學和機器學習領域也有15年的實務經驗。他的最新著作《應用深度學習 - 理解深度神經網絡的案例導向方法》（Applied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks）於2018年由Apress出版。他在人工智慧領域的研究非常活躍，並定期在領先的期刊上發表研究成果，並在國際會議上進行定期演講。

他在蘇黎世應用科技大學（Zurich University of Applied Sciences）和商業管理應用科技大學（HWZ University of Applied Sciences in Business Administration）擔任講師。他還負責Helsana Versicherung AG的人工智慧、研究和新技術。

他最近創立了TOELT LLC，這是一家旨在開發新的現代教學、輔導和研究方法以促進人工智慧的公司，目的是讓每個人都能接觸到人工智慧技術和研究。

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