
Practical Machine Learning for Computer Vision
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Content
- Intro
- Copyright
- Table of Contents
- Preface
- Who Is This Book For?
- How to Use This Book
- Organization of the Book
- Conventions Used in This Book
- Using Code Examples
- O'Reilly Online Learning
- How to Contact Us
- Acknowledgments
- Chapter 1. Machine Learning for Computer Vision
- Machine Learning
- Deep Learning Use Cases
- Summary
- Chapter 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
- Chapter 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
- 1x1 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
- Chapter 4. Object Detection and Image Segmentation
- Object Detection
- YOLO
- RetinaNet
- Segmentation
- Mask R-CNN and Instance Segmentation
- U-Net and Semantic Segmentation
- Summary
- Chapter 5. Creating Vision Datasets
- Collecting Images
- Photographs
- Imaging
- Proof of Concept
- Data Types
- Channels
- Geospatial Data
- Audio and Video
- Manual Labeling
- Multilabel
- Object Detection
- Labeling at Scale
- Labeling User Interface
- Multiple Tasks
- Voting and Crowdsourcing
- Labeling Services
- Automated Labeling
- Labels from Related Data
- Noisy Student
- Self-Supervised Learning
- Bias
- Sources of Bias
- Selection Bias
- Measurement Bias
- Confirmation Bias
- Detecting Bias
- Creating a Dataset
- Splitting Data
- TensorFlow Records
- Reading TensorFlow Records
- Summary
- Chapter 6. Preprocessing
- Reasons for Preprocessing
- Shape Transformation
- Data Quality Transformation
- Improving Model Quality
- Size and Resolution
- Using Keras Preprocessing Layers
- Using the TensorFlow Image Module
- Mixing Keras and TensorFlow
- Model Training
- Training-Serving Skew
- Reusing Functions
- Preprocessing Within the Model
- Using tf.transform
- Data Augmentation
- Spatial Transformations
- Color Distortion
- Information Dropping
- Forming Input Images
- Summary
- Chapter 7. Training Pipeline
- Efficient Ingestion
- Storing Data Efficiently
- Reading Data in Parallel
- Maximizing GPU Utilization
- Saving Model State
- Exporting the Model
- Checkpointing
- Distribution Strategy
- Choosing a Strategy
- Creating the Strategy
- Serverless ML
- Creating a Python Package
- Submitting a Training Job
- Hyperparameter Tuning
- Deploying the Model
- Summary
- Chapter 8. Model Quality and Continuous Evaluation
- Monitoring
- TensorBoard
- Weight Histograms
- Device Placement
- Data Visualization
- Training Events
- Model Quality Metrics
- Metrics for Classification
- Metrics for Regression
- Metrics for Object Detection
- Quality Evaluation
- Sliced Evaluations
- Fairness Monitoring
- Continuous Evaluation
- Summary
- Chapter 9. Model Predictions
- Making Predictions
- Exporting the Model
- Using In-Memory Models
- Improving Abstraction
- Improving Efficiency
- Online Prediction
- TensorFlow Serving
- Modifying the Serving Function
- Handling Image Bytes
- Batch and Stream Prediction
- The Apache Beam Pipeline
- Managed Service for Batch Prediction
- Invoking Online Prediction
- Edge ML
- Constraints and Optimizations
- TensorFlow Lite
- Running TensorFlow Lite
- Processing the Image Buffer
- Federated Learning
- Summary
- Chapter 10. Trends in Production ML
- Machine Learning Pipelines
- The Need for Pipelines
- Kubeflow Pipelines Cluster
- Containerizing the Codebase
- Writing a Component
- Connecting Components
- Automating a Run
- Explainability
- Techniques
- Adding Explainability
- No-Code Computer Vision
- Why Use No-Code?
- Loading Data
- Training
- Evaluation
- Summary
- Chapter 11. Advanced Vision Problems
- Object Measurement
- Reference Object
- Segmentation
- Rotation Correction
- Ratio and Measurements
- Counting
- Density Estimation
- Extracting Patches
- Simulating Input Images
- Regression
- Prediction
- Pose Estimation
- PersonLab
- The PoseNet Model
- Identifying Multiple Poses
- Image Search
- Distributed Search
- Fast Search
- Better Embeddings
- Summary
- Chapter 12. Image and Text Generation
- Image Understanding
- Embeddings
- Auxiliary Learning Tasks
- Autoencoders
- Variational Autoencoders
- Image Generation
- Generative Adversarial Networks
- GAN Improvements
- Image-to-Image Translation
- Super-Resolution
- Modifying Pictures (Inpainting)
- Anomaly Detection
- Deepfakes
- Image Captioning
- Dataset
- Tokenizing the Captions
- Batching
- Captioning Model
- Training Loop
- Prediction
- Summary
- Afterword
- Index
- About the Authors
- Colophon
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