Main Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection

Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection

0 / 0
How much do you like this book?
What’s the quality of the file?
Download the book for quality assessment
What’s the quality of the downloaded files?
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. InAdvanced 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.


Year:
2019
Publisher:
Apress
Language:
english
Pages:
294
ISBN 10:
1484249755
ISBN 13:
9781484249758
File:
PDF, 6.73 MB

You may be interested in Powered by Rec2Me

 

Most frequently terms

 
0 comments
 

You can write a book review and share your experiences. Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.