Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection

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.

開發和優化具有先進架構的深度學習模型。本書教授卷積神經網絡核心算法的細節和微妙之處。在《高級應用深度學習》中，您將學習使用Keras和TensorFlow進行卷積神經網絡和物體檢測的高級主題。

在學習過程中，您將研究卷積神經網絡的基本操作，如卷積和池化，然後研究更高級的架構，如inception網絡、resnet等。雖然本書討論了理論性的主題，但您將發現如何使用Keras進行高效工作，包括許多技巧和提示，例如如何使用自定義回調類在Keras中自定義日誌記錄，什麼是即時執行以及如何在模型中使用它。

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。通過本書，您將在Keras中實現各種模型，並學習許多高級技巧，將您的技能提升到更高的水平。

本書的學習內容包括：

- 瞭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盤上
- 暫停訓練並在稍後階段重新開始
- 在代碼中使用硬件加速（GPU）
- 使用TensorFlow的數據集抽象和預訓練模型以及遷移學習
- 從預訓練網絡中刪除和添加層，以適應特定項目
- 將預訓練模型（如Alexnet和VGG16）應用於新數據集

本書適合具有中級至高級Python和機器學習知識的科學家和研究人員。此外，需要具備中級的Keras和TensorFlow知識。

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.

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方面的專家。除了在喬治華盛頓大學（美國）和奧格斯堡大學（德國）擁有多年的研究經驗外，他在數據倉庫、數據科學和機器學習領域擁有15年的實踐經驗。他的最新著作《應用深度學習-基於案例的深度神經網絡理解方法》於2018年由Apress出版。他在人工智能領域的研究非常活躍，定期在領先期刊上發表研究成果並在國際會議上發表演講。

他在蘇黎世應用科學大學和HWZ應用科學大學擔任講師。他還負責Helsana Vesicherung AG的人工智能、研究和新技術。

他最近創辦了TOELT LLC，該公司旨在開發新的現代教學、輔導和研究方法，使人工智能技術和研究對每個人都更加可及。