Practical Machine Learning for Computer Vision End-To-End Machine Learning for Images
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 Gorner, 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
Publisher Name | OReilly Media |
---|---|
Author Name | Hagendorf, Col |
Format | Audio |
Bisac Subject Major | COM |
Language | NG |
Isbn 10 | 1098102363 |
Isbn 13 | 9781098102364 |
Target Age Group | min:NA, max:NA |
Dimensions | 00.00" H x 00.00" L x 00.00" W |
Page Count | 350 |
Valliappa (Lak) Lakshmanan is a Tech Lead for Big Data and Machine Learning Professional Services on Google Cloud Platform. His mission is to democratize machine learning so that it can be done by anyone anywhere using Google's amazing infrastructure (i.e., without deep knowledge of statistics or programming or ownership of lots of hardware). Martin Grner is passionate about science, technology, coding, algorithms and everything in between. He graduated from Mines Paris Tech with a major in computer vision, enjoyed his first engineering years in the computer architecture group of ST Microlectronics and then spent the next 11 years shaping the nascent eBook market, starting with the Mobipocket startup, which later became the software part of the Amazon Kindle and its mobile variants. He joined Google Developer Relations in 2011 and now focuses on parallel processing and machine learning (Dataflow and Tensorflow). Ryan Gilliard is an experienced research scientist with a demonstrated history of working in the hospital & health care industry. He is highly skilled in machine learning, artificial intelligence, optimization, and simulation with over 15 years of programming experience.