Umberto Michelucci Advanced Applied Deep Learning 9781484249758

Варианты приобретения

Цена: 41920.00T
Кол-во:
Наличие: Поставка под заказ. Есть в наличии на складе поставщика.
Склад Америка: 172 шт.
При оформлении заказа до: 2025-07-28
Ориентировочная дата поставки: Август-начало Сентября
При условии наличия книги у поставщика.

Добавить в корзину

в Мои желания

Автор: Umberto Michelucci
Название: Advanced Applied Deep Learning
ISBN: 9781484249758
Издательство: Springer
Классификация:

Вычислительная техника и информационные технологии

Языки программирования и описания сценариев: общие сведения

Искусственный интеллект

ISBN-10: 1484249755
Обложка/Формат: Soft cover
Страницы: 285
Вес: 0.47 кг.
Дата издания: 2019
Язык: English
Издание: 1st ed.
Иллюстрации: 15 illustrations, color; 73 illustrations, black and white; xviii, 288 p. 88 illus., 15 illus. in color.
Размер: 234 x 156 x 16
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Подзаголовок: Convolutional Neural Networks and Object Detection
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: 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 LearnSee how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is ForScientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.

Дополнительное описание: Chapter 1: Introduction and Development Environment Setup.- Chapter 2: TensorFlow: advanced topics.- Chapter 3: Fundamentals of Convolutional Neural Networks.- Chapter 4: Advanced CNNs and Transfer Learning.- Chapter 5: Cost functions and style trans

Advanced Applied Deep Learning, Umberto Michelucci