Код товара: 5192408148
Постоплата
Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras
Тип книги:
Печать по требованию
О товаре
Перейти к описанию
Автор на обложке
Dipanjan Sarkar; Raghav Bali; Tamoghna Ghosh
Год выпуска
2026
Тип бумаги в книге
Офсетная
Тип обложки
Мягкая обложка
Тип книги
Печать по требованию
О книге
Печатная книга «Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras» в мягкой обложке, язык - английский. В книге 438 страниц,
1 714 ₽ 2 346 ₽
Магазин
Подписаться
Т8
Перейти
4.9
Чат
Заказы
49.7 K
О магазине
Часто задаваемые вопросы
Описание
Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and KerasПечатная книга «Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras» в мягкой обложке, язык - английский. В книге 438 страниц, она напечатана на белой офсетной бумаге плотностью 80 г/м². Формат издания — 205x260 мм. Все книги аккуратно упаковываем в прочный трёхслойный непрозрачный картонный конверт.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem
Key Features
Build deep learning models with transfer learning principles in Python
implement transfer learning to solve real-world research problems
Perform complex operations such as image captioning neural style transfer
Book Description
Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.
The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.
The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).
By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.
What you will learn
Set up your own DL environment with graphics processing unit (GPU) and Cloud support
Delve into transfer learning principles with ML and DL models
Explore various DL architectures, including CNN, LSTM, and capsule networks
Learn about data and network representation and loss functions
Get to grips with models and strategies in transfer learning
Walk through potential challenges in building complex transfer learning models from scratch
Explore real-world research problems related to computer vision and audio analysis
Understand how transfer learning can be leveraged in NLP
Packt — это книги, написанные разработчиками для разработчиков, – экспертные знания, которые вы больше нигде не найдете в Интернете
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem
Key Features
Build deep learning models with transfer learning principles in Python
implement transfer learning to solve real-world research problems
Perform complex operations such as image captioning neural style transfer
Book Description
Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.
The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.
The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).
By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.
What you will learn
Set up your own DL environment with graphics processing unit (GPU) and Cloud support
Delve into transfer learning principles with ML and DL models
Explore various DL architectures, including CNN, LSTM, and capsule networks
Learn about data and network representation and loss functions
Get to grips with models and strategies in transfer learning
Walk through potential challenges in building complex transfer learning models from scratch
Explore real-world research problems related to computer vision and audio analysis
Understand how transfer learning can be leveraged in NLP
Packt — это книги, написанные разработчиками для разработчиков, – экспертные знания, которые вы больше нигде не найдете в Интернете
- Артикул
- 5192408148
- Автор на обложке
- Dipanjan Sarkar; Raghav Bali; Tamoghna Ghosh
- Год выпуска
- 2026
- Тип бумаги в книге
- Офсетная
- Тип обложки
- Мягкая обложка
- Тип книги
- Печать по требованию
- Язык издания
- Английский
- Количество страниц
- 438
- Размеры, мм
- 205x260
- Вес товара, г
- 1057
- ISBN
- 9781788831307
- Возрастные ограничения
- 12+
- Документы качества:
- Товар не подлежит обязательной сертификации
Подборки товаров в категории Книги: Компьютерные технологии
Отзывы о товаре
1
Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras
Показать сначала:
новые и полезные
с высокой оценкой
А
Имя скрыто
6 апреля 2023
Отзыв устарел
Рейтинга пока нет. Он формируется на основе актуальных отзывов на заказанные и оплаченные товары
По мнению покупателей
Качество бумаги:
отличное
Внешнее оформление:
красивое
Подача материала и манера письма:
очень увлекательная
Как правильно задавать вопросы?
Будьте вежливы и спрашивайте о товаре, на карточке которого вы находитесь
Если вы обнаружили ошибку в описанием товара, воспользуйтесь функцией
Как отвечать на вопросы?
Отвечать на вопросы могут клиенты, купившие товар, и официальные представители.
Выбрать «Лучший ответ» может только автор вопроса, если именно этот ответ ему помог.