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Advanced Applied Deep Learning - Convolutional Neural Networks and Object Detection (Paperback, 1st ed.)
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Advanced Applied Deep Learning - Convolutional Neural Networks and Object Detection (Paperback, 1st ed.)
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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 For Scientists and researchers with
intermediate-to-advanced Python and machine learning know-how.
Additionally, intermediate knowledge of Keras and TensorFlow is
expected.
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