Practical Machine Learning for Computer Vision

Valliappa (Lak) Lakshmanan is Google Cloud's director of analytics and AI solutions, where he oversees a team that develops cross-industry solutions to business problems. His objective is to democratize machine learning so that anyone, anywhere can perform it.

Martin Görner is a Keras/TensorFlow product manager who is focused on improving the developer experience when utilizing cutting-edge models. He is deeply interested in science, technology, code, algorithms, and everything in between.

Ryan Gillard works as an AI engineer in Google Cloud Professional Services, where he creates machine learning models for a number of businesses. He began his work in the hospital and healthcare industries as a research scientist. He enjoys working at the interface of neuroscience and physics, where he can explore intelligence through mathematics.

Practical Machine Learning for Computer Vision demonstrates how to use machine learning models to extract information from photos. ML engineers and data scientists will learn how to use proven ML techniques to handle a variety of picture problems such as classification, object identification, autoencoders, image synthesis, counting, and captioning. This book is an excellent introduction to deep learning from start to finish, including dataset development, data preprocessing, model construction, model training, assessment, deployment, and interpretability.

Valliappa Lakshmanan, Martin Görner, and Ryan Gillard of Google show you how to construct accurate and explainable computer vision ML models and put them into large-scale production utilizing strong ML architecture in a flexible and maintainable manner. You'll learn how to use TensorFlow or Keras models to create, train, assess, and predict.

You'll discover how to:

• Create a machine learning architecture for computer vision tasks.
• Choose an appropriate model (such as ResNet, SqueezeNet, or EfficientNet) for your purpose.
• Create an end-to-end machine learning pipeline for training, evaluating, deploying, and explaining your model.
• Preprocess photos for data enhancement and learnability.
• Include recommended practices for explainability and responsible AI.
• Image models can be deployed as web services or on edge devices.
• ML models must be monitored and managed.

Author: Martin Görner, Ryan Gillard and Valliappa Lakshmanan

Link to buy: https://www.amazon.com/Practical-Machine-Learning-Computer-Vision/dp/1098102363/

Ratings: 4.5 out of 5 stars (from 16 reviews)

Best Sellers Rank: #97,668 in Books

#13 in Computer Vision & Pattern Recognition

#21 in Computer Neural Networks

#30 in Artificial Intelligence (Books)