Practical Machine Learning for Computer Vision Valliappa Lakshmanan, Martin Görner, Ryan Gillard

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Spis treści książki

• Preface
  • Who Is This Book For?
  • How to Use This Book
  • Organization of the Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 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
      • Prediction function
      • Activation function
      • Optimizer
      • Training loss
      • Error metrics
    • Training the Model
      • Creating the datasets
      • Creating and viewing the model
      • Fitting the model
      • Plotting predictions
  • A Neural Network Using Keras
    • Neural Networks
      • Hidden layers
      • Training the neural network
      • Learning rate
      • Regularization
      • Early stopping
      • Hyperparameter tuning
    • Deep Neural Networks
      • Building a DNN
      • Dropout
      • Batch normalization
  • Summary
  • Glossary
• 3. Image Vision
  • Pretrained Embeddings
    • Pretrained Model
    • Transfer Learning
    • Fine-Tuning
      • Learning rate schedule
      • Differential learning rate
  • 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
      • Depthwise convolutions
      • Inverted residual bottlenecks
      • MobileNetV2
      • EfficientNet: Putting it all together
  • Beyond Convolution: The Transformer Architecture
  • Choosing a Model
    • Performance Comparison
    • Ensembling
    • Recommended Strategy
  • Summary
• 4. Object Detection and Image Segmentation
  • Object Detection
    • YOLO
      • YOLO grid
      • Object detection head
      • Loss function
      • YOLO limitations
    • RetinaNet
      • Feature pyramid networks
      • Anchor boxes
      • Architecture
      • Focal loss (for classification)
      • Smooth L1 loss (for box regression)
      • Non-maximum suppression
      • Other considerations
  • Segmentation
    • Mask R-CNN and Instance Segmentation
      • Region proposal networks
      • R-CNN
      • ROI resampling (ROI alignment)
      • Class and bounding box predictions
      • Transposed convolutions
      • Instance segmentation
    • U-Net and Semantic Segmentation
      • Images and labels
      • Architecture
      • Training
  • Summary
• 5. Creating Vision Datasets
  • Collecting Images
    • Photographs
    • Imaging
      • Polar grids
      • Satellite channels
      • Geospatial layers
    • Proof of Concept
  • Data Types
    • Channels
      • Scaling
      • Channel order
      • Grayscale
    • Geospatial Data
      • Raster data
      • Remote sensing
    • Audio and Video
      • Spectrogram
      • Frame by frame
      • Conv3D
  • 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
      • Running at scale
      • TensorFlow Recorder
    • Reading TensorFlow Records
  • Summary
• 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
      • Writing the Beam pipeline
      • Transforming the data
      • Saving the transform
      • Reading the preprocessed data
      • Transformation during serving
      • Benefits of tf.transform
  • Data Augmentation
    • Spatial Transformations
    • Color Distortion
    • Information Dropping
  • Forming Input Images
  • Summary
• 7. Training Pipeline
  • Efficient Ingestion
    • Storing Data Efficiently
      • TensorFlow Records
      • Storing preprocessed data
    • Reading Data in Parallel
      • Parallelizing
      • Measuring performance
    • Maximizing GPU Utilization
      • Efficient data handling
      • Vectorization
      • Staying in the graph
        • Iteration
        • Slicing and conditionals
        • Matrix math
        • Batching
  • Saving Model State
    • Exporting the Model
      • Invoking the model
      • Usable signature
      • Using the signature
    • Checkpointing
  • Distribution Strategy
    • Choosing a Strategy
    • Creating the Strategy
      • MirroredStrategy
      • MultiWorkerMirroredStrategy
        • Shuffling
        • Virtual epochs
      • TPUStrategy
  • Serverless ML
    • Creating a Python Package
      • Reusable modules
      • Invoking Python modules
      • Installing dependencies
    • Submitting a Training Job
      • Running on multiple GPUs
      • Distribution to multiple GPUs
      • Distribution to TPU
    • Hyperparameter Tuning
      • Specifying the search space
      • Using parameter values
      • Reporting accuracy
      • Result
      • Continuing tuning
    • Deploying the Model
  • Summary
• 8. Model Quality and Continuous Evaluation
  • Monitoring
    • TensorBoard
    • Weight Histograms
    • Device Placement
    • Data Visualization
    • Training Events
  • Model Quality Metrics
    • Metrics for Classification
      • Binary classification
      • Multiclass, single-label classification
      • Multiclass, multilabel classification
    • Metrics for Regression
    • Metrics for Object Detection
  • Quality Evaluation
    • Sliced Evaluations
    • Fairness Monitoring
    • Continuous Evaluation
  • Summary
• 9. Model Predictions
  • Making Predictions
    • Exporting the Model
    • Using In-Memory Models
    • Improving Abstraction
    • Improving Efficiency
  • Online Prediction
    • TensorFlow Serving
      • Deploying the model
      • Making predictions
    • Modifying the Serving Function
      • Changing the default signature
      • Multiple signatures
    • Handling Image Bytes
      • Loading the model
      • Adding a prediction signature
      • Exporting signatures
      • Base64 encoding
  • 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
• 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
      • LIME
      • KernelSHAP
      • Integrated Gradients
      • xRAI
    • Adding Explainability
      • Explainability signatures
      • Explanation metadata
      • Deploying the model
      • Obtaining explanations
  • No-Code Computer Vision
    • Why Use No-Code?
    • Loading Data
    • Training
    • Evaluation
  • Summary
• 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
• 12. Image and Text Generation
  • Image Understanding
    • Embeddings
    • Auxiliary Learning Tasks
    • Autoencoders
      • Architecture
      • Training
      • Latent vectors
    • Variational Autoencoders
      • Architecture
      • Loss
  • Image Generation
    • Generative Adversarial Networks
      • Creating the networks
      • Discriminator training
      • Generator training
      • Distribution changes
    • GAN Improvements
      • Conditional GANs
        • The cGAN generator
        • The cGAN discriminator
    • Image-to-Image Translation
    • Super-Resolution
    • Modifying Pictures (Inpainting)
    • Anomaly Detection
    • Deepfakes
  • Image Captioning
    • Dataset
    • Tokenizing the Captions
    • Batching
    • Captioning Model
      • Image encoder
      • Attention mechanism
      • Caption decoder
    • Training Loop
    • Prediction
  • Summary
• Afterword
• Index

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