This practical book shows you how to employ machine learning models to
extract information from images. ML engineers and data scientists will
learn how to solve a variety of image problems including classification,
object detection, autoencoders, image generation, counting, and
captioning with proven ML techniques. This book provides a great
introduction to end-to-end deep learning: dataset creation, data
preprocessing, model design, model training, evaluation, deployment, and
interpretability.
Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard
show you how to develop accurate and explainable computer vision ML
models and put them into large-scale production using robust ML
architecture in a flexible and maintainable way. You'll learn how to
design, train, evaluate, and predict with models written in TensorFlow
or Keras.
You'll learn how to:
- Design ML architecture for computer vision tasks
- Select a model (such as ResNet, SqueezeNet, or EfficientNet)
appropriate to your task
- Create an end-to-end ML pipeline to train, evaluate, deploy, and
explain your model
- Preprocess images for data augmentation and to support learnability
- Incorporate explainability and responsible AI best practices
- Deploy image models as web services or on edge devices
- Monitor and manage ML models
