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dc.contributor.authorMichelucci, Umberto-
dc.date.accessioned2019-05-02T13:38:09Z-
dc.date.available2019-05-02T13:38:09Z-
dc.date.issued2019-10-15-
dc.identifier.isbn978-1-4842-4975-8de_CH
dc.identifier.isbn978-1-4842-4976-5de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/16969-
dc.description.abstractDevelop 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.de_CH
dc.format.extent350de_CH
dc.language.isoende_CH
dc.publisherApressde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectMachine learningde_CH
dc.subjectDeep learningde_CH
dc.subjectPythonde_CH
dc.subjectTensorFlowde_CH
dc.subjectConvolutional neural networksde_CH
dc.subjectNeural networksde_CH
dc.subjectObject detectionde_CH
dc.subject.ddc004: Informatikde_CH
dc.titleAdvanced applied deep learning : convolutional neural networks and object detectionde_CH
dc.typeBuchde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Angewandte Simulation (IAS)de_CH
zhaw.publisher.placeBerkeleyde_CH
zhaw.edition1st editionde_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewEditorial reviewde_CH
Appears in Collections:Publikationen Life Sciences und Facility Management

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