|Title:||Advanced applied deep learning : convolutional neural networks and object detection|
|Authors :||Michelucci, Umberto|
|Publisher / Ed. Institution :||Apress|
|Publisher / Ed. Institution:||Berkeley|
|License (according to publishing contract) :||Licence according to publishing contract|
|Type of review:||Editorial review|
|Subjects :||Machine learning; Deep learning; Python; TensorFlow; Convolutional neural networks; Neural networks; Object detection|
|Subject (DDC) :||004: Computer science|
|Abstract:||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.|
|Departement:||Life Sciences and Facility Management|
|Organisational Unit:||Institute of Applied Simulation (IAS)|
|Appears in Collections:||Publikationen Life Sciences und Facility Management|
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