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▼a Zafar, Iffat. |
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▼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 | 0 |
▼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 | 8 |
▼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 | 8 |
▼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 | 8 |
▼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 | 8 |
▼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 | 0 |
▼a Neural networks (Computer science)
▼x Computer simulation. |
| 650 | 7 |
▼a Neural networks (Computer science)
▼x Computer simulation.
▼2 fast
▼0 (OCoLC)fst01036261 |
| 655 | 4 |
▼a Electronic books. |
| 700 | 1 |
▼a Tzanidou, Giounona. |
| 700 | 1 |
▼a Burton, Richard. |
| 700 | 1 |
▼a Patel, Nimesh. |
| 700 | 1 |
▼a Araujo, Leonardo. |
| 776 | 08 |
▼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 |
| 856 | 40 |
▼3 EBSCOhost
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