By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. Google engineers Valliappa Lakshmanan, Martin Garner, 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/Keras. This book also covers best practices to improve the operationalization of the models using end-to-end ML pipelines. 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
Practical Machine Learning for Computer Vision: End-to-end Machine Learning for Images
But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How?
This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.
You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset.
Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning.
This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book.
Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.About This Book* Load, store, edit, and visualize data using OpenCV and Python* Grasp the fundamental concepts of classification, ...
This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning.
This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table ...
This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust.