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  190608s2019 xx o 000 0 eng d

• 019

  ▼a  1103693554
  ▼a  1103982098

• 020

  ▼a  1788839269

• 020

  ▼a  9781788839266
  ▼q  (electronic bk.)

• 035

  ▼a  2149484
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1104086471
  ▼z  (OCoLC)1103693554
  ▼z  (OCoLC)1103982098

• 040

  ▼a  EBLCP
  ▼b  eng
  ▼c  EBLCP
  ▼d  YDX
  ▼d  N$T
  ▼d  248023

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  ▼a  TA1634

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  ▼a  COM
  ▼x  000000
  ▼2  bisacsh

• 082

  ▼a  006.37
  ▼2  23

• 100

  ▼a  Planche, Benjamin.

• 245

  ▼a  Hands-On Computer Vision with TensorFlow 2
  ▼h  [electronic resource]:
  ▼b  Leverage Deep Learning to Create Powerful Image Processing Apps with TensorFlow 2. 0 and Keras.

• 260

  ▼a  Birmingham:
  ▼b  Packt Publishing, Limited,
  ▼c  2019.

• 300

  ▼a  1 online resource (361 p.).

• 500

  ▼a  Description based upon print version of record.

• 500

  ▼a  Lack of spatial reasoning

• 505

  ▼a  Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision; Chapter 1: Computer Vision and Neural Networks; Technical requirements; Computer vision in the wild; Introducing computer vision; Main tasks and their applications; Content recognition; Object classification; Object identification; Object detection and localization; Object and instance segmentation; Pose estimation; Video analysis; Instance tracking; Action recognition; Motion estimation; Content-aware image edition

• 505

  ▼a  Scene reconstructionA brief history of computer vision; First steps to first successes; Underestimating the perception task; Hand-crafting local features; Adding some machine learning on top; Rise of deep learning; Early attempts and failures; Rise and fall of the perceptron; Too heavy to scale; Reasons for a comeback; The internet -- the new El Dorado of data science; More power than ever; Deep learning or the rebranding of artificial neural networks; What makes learning deep?; Deep learning era; Getting started with neural networks; Building a neural network; Imitating neurons

• 505

  ▼a  Biological inspirationMathematical model; Implementation; Layering neurons together; Mathematical model; Implementation; Applying our network to classification; Setting up the task; Implementing the network; Training a neural network; Learning strategies; Supervised learning; Unsupervised learning; Reinforcement learning; Teaching time; Evaluating the loss; Back-propagating the loss; Teaching our network to classify; Training considerations -- underfitting and overfitting; Summary; Questions; Further reading; Chapter 2: TensorFlow Basics and Training a Model; Technical requirements

• 505

  ▼a  Getting started with TensorFlow 2 and KerasIntroducing TensorFlow; TensorFlow main architecture; Introducing Keras; A simple computer vision model using Keras; Preparing the data; Building the model; Training the model; Model performance; TensorFlow 2 and Keras in detail; Core concepts; Introducing tensors; TensorFlow graph; Comparing lazy execution to eager execution; Creating graphs in TensorFlow 2; Introducing TensorFlow AutoGraph and tf.function; Backpropagating error using the gradient tape; Keras models and layers; Sequential and Functional APIs; Callbacks; Advanced concepts

• 505

  ▼a  How tf.function worksVariables in TensorFlow 2; Distribute strategies; Using the Estimator API; Available pre-made Estimators; Training a custom Estimator; TensorFlow ecosystem; TensorBoard; TensorFlow Addons and TensorFlow Extended; TensorFlow Lite and TensorFlow.js; Where to run your model; On a local machine; On a remote machine; On Google Cloud; Summary; Questions; Chapter 3: Modern Neural Networks; Technical requirements; Discovering convolutional neural networks; Neural networks for multidimensional data; Problems with fully-connected networks; Explosive number of parameters

• 520

  ▼a  Computer vision is achieving a new frontier of capabilities in fields like health, automobile or robotics. This book explores TensorFlow 2, Google's open-source AI framework, and teaches how to leverage deep neural networks for visual tasks. It will help you acquire the insight and skills to be a part of the exciting advances in computer vision.

• 590

  ▼a  Master record variable field(s) change: 050, 072, 082, 630, 650

• 630

  ▼a  TensorFlow.

• 650

  ▼a  Computer vision.

• 650

  ▼a  Machine learning.

• 650

  ▼a  COMPUTERS / General.
  ▼2  bisacsh

• 655

  ▼a  Electronic books.

• 700

  ▼a  Andres, Eliot.

• 776

  ▼i  Print version:
  ▼a  Planche, Benjamin
  ▼t  Hands-On Computer Vision with TensorFlow 2 : Leverage Deep Learning to Create Powerful Image Processing Apps with TensorFlow 2. 0 and Keras.
  ▼d  Birmingham : Packt Publishing, Limited,c2019,
  ▼z  9781788830645

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2149484

• 938

  ▼a  EBL - Ebook Library
  ▼b  EBLB
  ▼n  EBL5783101

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  16253192

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2149484

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T