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Comprehensive Book for Machine Learning on Images: Review

Comprehensive Book for Machine Learning on Images: Review

14 recent reviews.

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.

Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images:
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  • Best purchase

    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

    Sentiment
    • Positive
    Content
    • Book Quality
  • Waste of money

    This book is bad. It gives you not structured pieces of information that you cannot reproduce.
    This books sends you to a GutHub repo that does not match this book. Even simple things like function names don't match. Even when you follow steps in the book you get errors that you need to debug.
    It is not a book that will help you learn tensor flow.
    Way overpriced for what it is. Actually you will be more successful if you refer to tensor website for examples.

    Sentiment
    • Negative
    Content
    • Reproducibility and Errors
  • Great book for any Computer Vision Practitioner!

    I loved that this book essentially built on top of my current knowledge of Computer Vision. I have been through many courses to learn a lot about Computer Vision. The number one thing I liked about this book is that it provided a lot of context to various questions I have had but never got the chance to research. Things like how to handle Polar vs Cartesian Coordinates on images, how to handle other metadata related images, how to perform CV on sound waves, and etc.The amount of additional resources this book has makes it well worth the price! I highly recommend this book if you work in the Computer Vision or even in the ML space.

    Sentiment
    • Positive
    Content
    • Additional Resources and Context
  • Expensive book, but worth your money

    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.

    Sentiment
    • Positive
    Content
    • Machine Learning Concepts
  • Canonical guide for ML on images

    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.

    Sentiment
    • Positive
    Content
    • Machine Learning Concepts
  • Easily understandable helpful and for both beginners and both advanced ML practitioners

    Easily understandable helpful and for both beginners and both advanced ML practitioners

    Sentiment
    • Positive
    Content
    • Book Quality
  • Terrific, comprehensive resource for understanding and applying ML for computer vision problems

    This is a really well written, comprehensive, and approachable book for anyone wanting to build machine learning applications on images. It does a great job of introducing the ML concepts and provides lots of hands on examples and code snippets to be up and running. It packs a bunch of best-practices and tips that you can only learn by years of hands-on experimentation, and the authors share these from their vast experience building these methods and teaching them.

    Sentiment
    • Positive
    Content
    • Content and Explanations
  • Just received this book and the images are in black and white

    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?

    Sentiment
    • None
    Content
    • Color of Figures and Code
  • Detalied

    Very detailed Computer Vision book, good quality book but it has two downsides:
    1- It has an over the price I paid $85CA,
    2- The models after training and doing all that it required, the author did not bring the trained model to the production part.
    I returned the book because I know most of the knowledge in it I wanted to learn how to reach the production level.
    My conclusion is if you want to learn computer vision, as a student or researcher it is really good, if you work in the industry, it is not complete.

    Sentiment
    • Positive
    Content
    • Practical Aspects of ML Workflow
  • Nice book, but the black & white images are a letdown

    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

    Sentiment
    • None
    Content
    • Color of Figures and Code
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72 Net Promoter
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