Deep Learning for Computer Vision with Python — Starter Bundle

The Starter Bundle begins with a gentle introduction to the world of computer vision and machine learning, builds to neural networks, and then turns full steam into deep learning and Convolutional Neural Networks. You'll even solve fun and interesting real-world problems using deep learning along the way. 1 Introduction......Page 13 2.1 What Is Data Augmentation?......Page 15 2.2 Visualizing Data Augmentation......Page 16 2.3.1 The Flowers-17 Dataset......Page 19 2.3.2 Aspect-aware Preprocessing......Page 20 2.3.3 Flowers-17: No Data Augmentation......Page 23 2.3.4 Flowers-17: With Data Augmentation......Page 27 2.4 Summary......Page 31 3 Networks as Feature Extractors......Page 33 3.1 Extracting Features with a Pre-trained CNN......Page 34 3.1.1 What Is HDF5?......Page 35 3.1.2 Writing Features to an HDF5 Dataset......Page 36 3.2 The Feature Extraction Process......Page 39 3.2.1 Extracting Features From Animals......Page 43 3.2.3 Extracting Features From Flowers-17......Page 44 3.3 Training a Classifier on Extracted Features......Page 45 3.3.2 Results on CALTECH-101......Page 47 3.4 Summary......Page 48 4.1 Ranked Accuracy......Page 51 4.1.1 Measuring rank-1 and rank-5 Accuracies......Page 53 4.1.2 Implementing Ranked Accuracy......Page 54 4.2 Summary......Page 56 5.1 Transfer Learning and Fine-tuning......Page 59 5.1.1 Indexes and Layers......Page 62 5.1.2 Network Surgery......Page 63 5.1.3 Fine-tuning, from Start to Finish......Page 65 5.2 Summary......Page 71 6.1 Ensemble Methods......Page 73 6.1.1 Jensen’s Inequality......Page 74 6.1.2 Constructing an Ensemble of CNNs......Page 75 6.1.3 Evaluating an Ensemble......Page 79 6.2 Summary......Page 82 7.1 Adaptive Learning Rate Methods......Page 85 7.1.2 Adadelta......Page 86 7.1.4 Adam......Page 87 7.2.1 Three Methods You Should Learn how to Drive: SGD, Adam, and RMSprop......Page 88 7.3 Summary......Page 89 8.1 A Recipe for Training......Page 91 8.2 Transfer Learning or Train from Scratch......Page 95 8.3 Summary......Page 96 9.1 Downloading Kaggle: Dogs vs. Cats......Page 97 9.2 Creating a Configuration File......Page 98 9.2.1 Your First Configuration File......Page 99 9.3 Building the Dataset......Page 100 9.4 Summary......Page 104 10.1 Additional Image Preprocessors......Page 105 10.1.1 Mean Preprocessing......Page 106 10.1.2 Patch Preprocessing......Page 107 10.1.3 Crop Preprocessing......Page 109 10.2 HDF5 Dataset Generators......Page 111 10.3 Implementing AlexNet......Page 114 10.4 Training AlexNet on Kaggle: Dogs vs. Cats......Page 119 10.5 Evaluating AlexNet......Page 122 10.6.1 Extracting Features Using ResNet......Page 125 10.6.2 Training a Logistic Regression Classifier......Page 129 10.7 Summary......Page 130 11 GoogLeNet......Page 133 11.1.1 Inception......Page 134 11.1.2 Miniception......Page 135 11.2 MiniGoogLeNet on CIFAR-10......Page 136 11.2.1 Implementing MiniGoogLeNet......Page 137 11.2.2 Training and Evaluating MiniGoogLeNet on CIFAR-10......Page 142 11.2.3 MiniGoogLeNet: Experiment #1......Page 145 11.2.4 MiniGoogLeNet: Experiment #2......Page 146 11.2.5 MiniGoogLeNet: Experiment #3......Page 147 11.3 The Tiny ImageNet Challenge......Page 148 11.3.2 The Tiny ImageNet Directory Structure......Page 149 11.3.3 Building the Tiny ImageNet Dataset......Page 150 11.4.1 Implementing DeeperGoogLeNet......Page 155 11.4.3 Creating the Training Script......Page 163 11.4.4 Creating the Evaluation Script......Page 165 11.4.5 DeeperGoogLeNet Experiments......Page 167 11.5 Summary......Page 170 12.1 ResNet and the Residual Module......Page 173 12.1.1 Going Deeper: Residual Modules and Bottlenecks......Page 174 12.1.2 Rethinking the Residual Module......Page 176 12.2 Implementing ResNet......Page 177 12.3 ResNet on CIFAR-10......Page 182 12.3.1 Training ResNet on CIFAR-10 With the ctrl + c Method......Page 183 12.3.2 ResNet on CIFAR-10: Experiment #2......Page 187 12.4 Training ResNet on CIFAR-10 with Learning Rate Decay......Page 190 12.5 ResNet on Tiny ImageNet......Page 194 12.5.1 Updating the ResNet Architecture......Page 195 12.5.2 Training ResNet on Tiny ImageNet With the ctrl + c Method......Page 196 12.5.3 Training ResNet on Tiny ImageNet with Learning Rate Decay......Page 200 12.6 Summary......Page 204