کتاب یادگیری ماشین کاربردی برای بینایی ماشین

یادگیری ماشین برای بینایی ماشین

Machine Learning • Deep Learning Use Cases • Summary

مدل‌های یادگیری ماشین برای بینایی

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

بینایی تصویر

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

تشخیص اشیاء و بخش‌بندی تصویر

Object Detection • YOLO • RetinaNet • Segmentation • Mask R-CNN and Instance Segmentation • U-Net and Semantic Segmentation • Summary

ایجاد مجموعه‌داده‌های بینایی

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

پیش‌پردازش

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

خط‌لوله آموزش

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

کیفیت مدل و ارزیابی مداوم

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

پیش‌بینی‌های مدل

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

روندها در یادگیری ماشین تولیدی

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

مسائل پیشرفته بینایی

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

تولید تصویر و متن

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