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 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 ForScientists and
researchers with intermediate-to-advanced Python and machine learning know-how. Additionally
intermediate knowledge of Keras and TensorFlow is expected.
