Advanced applied deep learning von michelucci (33 Ergebnisse)

Zustand: New.

Zustand: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.

Paperback or Softback. Zustand: New. Applied Deep Learning with Tensorflow 2: Learn to Implement Advanced Deep Learning Techniques with Python. Book.

Zustand: New. 2019. 1st ed. paperback. . . . . .

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: Brand New. 285 pages. 9.00x6.00x0.75 inches. In Stock.

Zustand: New.

Zustand: New.

Zustand: New. 2019. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

Zustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Zustand: New. 2nd ed. edition NO-PA16APR2015-KAP.

Zustand: New. 2022. 2nd ed. Paperback. . . . . .

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: New. 2022. 2nd ed. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Advanced Applied Deep Learning | Convolutional Neural Networks and Object Detection | Umberto Michelucci | Taschenbuch | xviii | Englisch | 2019 | Apress | EAN 9781484249758 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Applied Deep Learning with TensorFlow 2 | Learn to Implement Advanced Deep Learning Techniques with Python | Umberto Michelucci | Taschenbuch | xxviii | Englisch | 2022 | Apress | EAN 9781484280195 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

PF. Zustand: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Zustand: new. Questo è un articolo print on demand.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 datasetsWho 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. 304 pp. Englisch. …

PF. Zustand: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Paperback. Zustand: Brand New. 2nd edition. 380 pages. 10.00x7.00x1.00 inches. In Stock. This item is printed on demand.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 datasetsWho 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.…