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  ▼a  9781789132823
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  ▼z  1789130336

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  ▼z  9781789130331

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  ▼n  http://www.overdrive.com

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  ▼a  Zafar, Iffat.

• 245

  ▼a  Hands-On Convolutional Neural Networks with TensorFlow
  ▼h  [electronic resource]:
  ▼b  Solve Computer Vision Problems with Modeling in TensorFlow and Python.

• 260

  ▼a  Birmingham:
  ▼b  Packt Publishing Ltd,
  ▼c  2018.

• 300

  ▼a  1 online resource (264 p.).

• 336

  ▼a  text
  ▼2  rdacontent

• 337

  ▼a  computer
  ▼2  rdamedia

• 338

  ▼a  online resource
  ▼2  rdacarrier

• 500

  ▼a  Description based upon print version of record.

• 500

  ▼a  Substituting the 3x3 convolution

• 505

  ▼a  Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Setup and Introduction to TensorFlow; The TensorFlow way of thinking; Setting up and installing TensorFlow; Conda environments; Checking whether your installation works; TensorFlow API levels; Eager execution; Building your first TensorFlow model; One-hot vectors; Splitting into training and test sets; Creating TensorFlow graphs; Variables; Operations; Feeding data with placeholders; Initializing variables; Training our model; Loss functions; Optimization; Evaluating a trained model

• 505

  ▼a  The sessionSummary; Chapter 2: Deep Learning and Convolutional Neural Networks; AI and ML; Types of ML; Old versus new ML; Artificial neural networks; Activation functions; The XOR problem; Training neural networks; Backpropagation and the chain rule; Batches; Loss functions; The optimizer and its hyperparameters; Underfitting versus overfitting; Feature scaling; Fully connected layers; A TensorFlow example for the XOR problem; Convolutional neural networks; Convolution; Input padding; Calculating the number of parameters (weights); Calculating the number of operations

• 505

  ▼a  Converting convolution layers into fully connected layersThe pooling layer; 1x1 Convolution; Calculating the receptive field; Building a CNN model in TensorFlow; TensorBoard; Other types of convolutions; Summary; Chapter 3: Image Classification in TensorFlow; CNN model architecture; Cross-entropy loss (log loss); Multi-class cross entropy loss; The train/test dataset split; Datasets; ImageNet; CIFAR; Loading CIFAR; Image classification with TensorFlow; Building the CNN graph; Learning rate scheduling; Introduction to the tf.data API; The main training loop; Model Initialization

• 505

  ▼a  Do not initialize all weights with zerosInitializing with a mean zero distribution; Xavier-Bengio and the Initializer; Improving generalization by regularizing; L2 and L1 regularization; Dropout; The batch norm layer; Summary; Chapter 4: Object Detection and Segmentation; Image classification with localization; Localization as regression; TensorFlow implementation; Other applications of localization; Object detection as classification - Sliding window; Using heuristics to guide us (R-CNN); Problems; Fast R-CNN; Faster R-CNN; Region Proposal Network; RoI Pooling layer

• 505

  ▼a  Conversion from traditional CNN to Fully ConvnetsSingle Shot Detectors - You Only Look Once; Creating training set for Yolo object detection; Evaluating detection (Intersection Over Union); Filtering output; Anchor Box; Testing/Predicting in Yolo; Detector Loss function (YOLO loss); Loss Part 1; Loss Part 2; Loss Part 3; Semantic segmentation; Max Unpooling; Deconvolution layer (Transposed convolution); The loss function; Labels; Improving results; Instance segmentation; Mask R-CNN; Summary; Chapter 5: VGG, Inception Modules, Residuals, and MobileNets; Substituting big convolutions

• 520

  ▼a  Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!

• 650

  ▼a  Neural networks (Computer science)
  ▼x  Computer simulation.

• 650

  ▼a  Neural networks (Computer science)
  ▼x  Computer simulation.
  ▼2  fast
  ▼0  (OCoLC)fst01036261

• 655

  ▼a  Electronic books.

• 700

  ▼a  Tzanidou, Giounona.

• 700

  ▼a  Burton, Richard.

• 700

  ▼a  Patel, Nimesh.

• 700

  ▼a  Araujo, Leonardo.

• 776

  ▼i  Print version:
  ▼a  Zafar, Iffat
  ▼t  Hands-On Convolutional Neural Networks with TensorFlow : Solve Computer Vision Problems with Modeling in TensorFlow and Python.
  ▼d  Birmingham : Packt Publishing Ltd,c2018,
  ▼z  9781789130331

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  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1881049

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  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH35074140

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  ▼a  EBL - Ebook Library
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  ▼a  YBP Library Services
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