This comprehensive and approachable book provides hands-on examples and code snippets for building machine learning applications on images. It covers model architectures, transfer learning, object detection, image segmentation, training pipeline engineering, and advanced topics. While the book is well-constructed, the lack of colored figures and low-quality paper disappoint some readers. Despite this, the book is recommended for both beginners and advanced practitioners in computer vision and machine learning.
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It was just what I needed. It saves me so much time of running around finding medium articles to get theoretical knowledge for research based interviews
If you are starting in ML this books will help you with many of the fundamentals too. Well written and well planned book. They take you slowly from the fundamentals of CNN to ML Ops in production. I took their one star only for the price but book content is 5 star.
I'd recommend this book to anyone doing machine learning with image data. Whether you are a software developer just getting started with ML or have experience building custom models, this book has something for you. It covers everything from common architectures of vision models, types of image prediction tasks, how to process image data, training and evaluating image models, productionizing image models, and more.
Easily understandable helpful and for both beginners and both advanced ML practitioners
Both the figures and the code is black and white.
I have had other Oreilly books on machine learning (Hands-on Machine Learning and Deep Learning for Coders) and they both had colored figures and colored code.
Also, the paper quality is very low( not smooth like other oreilly coding books).
did I get a fake/cheaper version?
The book is well-written and the content is of high quality, however black and white pictures for a ML book on computer vision are a bit of a letdown. With color pictures I would have probably given 5 stars
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