Advanced Applied Deep Learning

Umberto Michelucci

ISBN 10: 1484249755 / ISBN 13: 9781484249758

Published by Apress, 2019

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first book with extensive examples of advanced deep learning techniques including CNNUses real-life datasets in the application of advanced techniques&nbspGuides you from easier examples to more advanced tech.

Seller Inventory # 288912960

Bibliographic Details

• Title: Advanced Applied Deep Learning
• Publisher: Apress
• Publication Date: 2019
• Language: English
• ISBN 10: 1484249755
• ISBN 13: 9781484249758
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

About the Author

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.