| Description |
1 online resource (264 pages) |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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! |
| Notes |
Substituting the 3x3 convolution |
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Print version record |
| Subject |
Neural networks (Computer science) -- Computer simulation
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Neural networks (Computer science) -- Computer simulation
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| Form |
Electronic book
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| Author |
Tzanidou, Giounona
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Burton, Richard
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Patel, Nimesh
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Araujo, Leonardo
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| ISBN |
9781789132823 |
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1789132827 |
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