Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images 1st Edition

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.

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

as a person with ads and not able to focus, this book has a way to attract me to know more, with context explaining why thing is the way it is and how to achieve certain things. It’s really changed my fear about ai

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?

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.

Highly recommend this book for understanding Computer Vision from the ground-up. This book empowered me to literally build a Satellite imagery ML pipeline from labeling to serving. The book offers exceptionally clear explanations of TensorFlow APIs (which are impossible to find with such a high signal-to-noise ratio elsewhere), and it provides practical advice at every stage of ML living up to the book's title. There are so many other books/tutorials in the CV that either spend so much time on first principles or traditional CV techniques or don't offer clear opinions as to how to approach a similar kind of problem in the real world. There's so much information dumped on Medium or Keras official site but, no two tutorials use the same data ingestion, or preprocessing techniques leaving one confused all the time. This book gives you practical heuristics at every stage of the process. For example, the authors offer clear opinions as to why transfer learning should be used instead of training a model from the ground up or which SOTA model to pick in different situations. and so many more practical recommendations that guide you while you build your own models or pipelines.

If you don't read through the entire lengthy description, you may not realize that the physical books is entirely in BLACK & WHITE. Yes, you read that correctly: a book on computer vision (where color is obviously a tremendously important dimension) is in black and white.Shame on O'reilly for destroying an otherwise great product by trying to save a couple bucks.

This is a well constructed book that enables you to work more efficiently with image analysis and computer vision techniques covering practical aspects of a machine learning workflow.The chapters are well laid out in a logical progression covering various model architectures with clear pictures and amazing explanations. The book moves from fundamentals to more advanced topics focusing on design and implementation covering best practices. For example, when talking about Training Pipeline they cover how to efficiently store and retrieve data from the storage layer, how to maximize GPU utilization, how to choose a distributed training strategy and more. Authors have decades of industry experience which is manifested when covering how to take a model from development to production, an often overlooked topic. These concepts are extremely useful to any ML practitioner looking to deploy their models in production.The authors complement the book with numerous and easy to follow examples, and make all code available via Github. This practical, hands-on approach allows readers to more easily grasp complex ideas and techniques. This text acts as an excellent resource for all levels of expertise including skill building and skill acquisition.