计算机视觉机器学习实践（影印版）

本书向你展示了如何 使用机器学习(ML)模型从 图像中提取信息。ML工 程师和数据科学家将学习 行之有效的ML技术来解 决各种图像问题，包括分 类、目标检测、自动编码 器、图像生成、计数和添 加字幕。本书很好地介绍 了端到端的深度学习：数 据集创建、数据预处理、 模型设计、模型训练、评 估、部署和可解释性。 Google工程师Valliappa Lakshmanan、Martin G? rner、Ryan Gillard为你展 示了如何开发精准且可解 释的计算机视觉ML模型 ，并以灵活且可维护的方 式使用健壮的ML架构将 其投入大规模生产。你将 学习如何使用TensorFlow 和Keras编写的模型进行 设计、训练、评估和预测 。

Preface 1. Machine Learning for Computer Vision Machine Learning Deep Learning Use Cases Summary 2. ML Models for Vision A Dataset for Machine Perception 5-Flowers Dataset Reading Image Data Visualizing Image Data Reading the Dataset File A Linear Model Using Keras Keras Model Training the Model A Neural Network Using Keras Neural Networks Deep Neural Networks Summary Glossary 3. Image Vision Pretrained Embeddings Pretrained Model Transfer Learning Fine-Tuning Convolutional Networks Convolutional Filters Stacking Convolutional Layers Pooling Layers AlexNet The Quest for Depth Filter Factorization lxl Convolutions VGG19 Global Average Pooling Modular Architectures Inception SqueezeNet ResNet and Skip Connections DenseNet Depth-Separable Convolutions Xception Neural Architecture Search Designs NASNet The MobileNet Family Beyond Convolution: The Transformer Architecture Choosing a Model Performance Comparison Ensembling Recommended Strategy Summary 4. Object Detection and Image Segmentation Object Detection YOLO RetinaNet Segmentation Mask R-CNN and Instance Segmentation U-Net and Semantic Segmentation Summary 5. Creating Visi0n Datasets Collecting Images Photographs 6. Preprocessing 7. Training Pipeline 8. Model Quality and Continuous Evaluation 9. Model Predictions 10. Trends in Production ML 11. Advanced Vision Problems 12. Image and Text Generation Afterword Index