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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.
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.
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.
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.
The first book with extensive examples of advanced deep learning techniques including CNN
Uses real-life datasets in the application of advanced techniques
Guides you from easier examples to more advanced techniques stepping up the difficulty and focusing on advanced methods
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 transfer.- Chapter 6: Object classification - an introduction.- Chapter 7: Object localization - an implementation in Python.- Chapter 8: Histology Tissue Classification
| Erscheinungsjahr: | 2019 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xviii
285 S. 60 s/w Illustr. 28 farbige Illustr. 285 p. 88 illus. 28 illus. in color. |
| ISBN-13: | 9781484249758 |
| ISBN-10: | 1484249755 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Michelucci, Umberto |
| Auflage: | First Edition |
| Hersteller: | APRESS |
| Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 17 mm |
| Von/Mit: | Umberto Michelucci |
| Erscheinungsdatum: | 29.09.2019 |
| Gewicht: | 0,464 kg |
The first book with extensive examples of advanced deep learning techniques including CNN
Uses real-life datasets in the application of advanced techniques
Guides you from easier examples to more advanced techniques stepping up the difficulty and focusing on advanced methods
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 transfer.- Chapter 6: Object classification - an introduction.- Chapter 7: Object localization - an implementation in Python.- Chapter 8: Histology Tissue Classification
| Erscheinungsjahr: | 2019 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xviii
285 S. 60 s/w Illustr. 28 farbige Illustr. 285 p. 88 illus. 28 illus. in color. |
| ISBN-13: | 9781484249758 |
| ISBN-10: | 1484249755 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Michelucci, Umberto |
| Auflage: | First Edition |
| Hersteller: | APRESS |
| Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 17 mm |
| Von/Mit: | Umberto Michelucci |
| Erscheinungsdatum: | 29.09.2019 |
| Gewicht: | 0,464 kg |
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