Thursday, July 20, 2017

Machine Learning for OpenCV now available

My new book Machine Learning for OpenCV is now available via Packt Publishing Ltd. The book features 382 pages filled with machine learning and image processing goodness, teaching you how to master key concepts of statistical learning using Python Anaconda, OpenCV, and scikit-learn.

This will be an introductory book for folks who are already familiar with OpenCV, but now want to dive into the world of machine learning. The goal is to illustrate the fundamental machine learning concepts using practical, hands-on examples.

As always, all source code is available for free on GitHub. The book is packed with examples on how to implement different techniques in OpenCV—such as classification, regression, k-NN, support vectort machines, decision trees, random forests, Bayes classifiers, k-means clustering, and neural networks.


By the end of this book, you will be ready to take on your own Machine Learning problems, either by building on the existing source code or developing your own algorithm from scratch!

Get it while it's hot! If you act fast, you can still get 20% off at packtpub.com by using promo code PACKT20. Or get it on Amazon and leave a review to tell me what you think!

The foreword to the book was written by Ariel Rokem, Senior Data Scientist at the University of Washington eScience Institute, a close colleague, collaborator, and mentor of mine. You can find out what he has to say about this book here.

The outline of the book is as follows:

  1. A Gentle Introduction to Machine Learning
  2. Working with Data Using OpenCV and Python
  3. First Steps in Supervised Learning
  4. Representing Data and Engineering Features
  5. Using Decision Trees to Make a Medical Diagnosis
  6. Detecting Pedestrians with Support Vector Machines
  7. Implementing a Spam Filter with Bayesian Learning
  8. Discovering Hidden Structures with Unsupervised Learning
  9. Using Deep Learning to Classify Handwritten Digits
  10. Combining Different Algorithms into an Ensemble
  11. Selecting the Right Model with Hyperparameter Tuning
  12. Wrapping Up

Stay tuned for example chapters and code samples!

Tuesday, June 6, 2017

Highlights and new discoveries in Neuroscience (May 2017)

In the latest edition of this monthly digest series, you can learn about bioengineered retinas for the visually impaired, how TV consumption is linked to developmental issues in children, and why our brain cells may prevent us from burning fat when we diet.

Monday, May 22, 2017

Flash sale: OpenCV with Python Blueprints for only $10

For a limited time only, you can get OpenCV with Python Blueprints and any other eBook or video for only $10 on the Packt website. That's a staggering 77% discount! The flash sale will be going on for only a few days, so if you've been toying with the idea of getting some of these books, make sure to act now.


Michael Beyeler
OpenCV with Python Blueprints
Design and develop advanced computer vision projects using OpenCV with Python


Packt Publishing Ltd., London, England
Paperback: 230 pages
ISBN 978-178528269-0
[GitHub] [Discussion Group] [Free Sample]

Sunday, May 14, 2017

Highlights and new discoveries in Neuroscience (April 2017)

In the latest edition of this monthly digest series you can learn how your brain activity changes under the influence of psychedelic drugs, why brain games won't actually make you smarter, and how gene therapy might be able to treat patients blinded from retinitis pigmentosa.

Tuesday, April 25, 2017

Pre-order now: Machine Learning for OpenCV

You might be wondering what I have been up to, since my blog has been quiet for a while. Don't worry, I'm still here!

Truth is, in the little spare time I seem to have these days, I've been working on a new book: Machine Learning for OpenCV, coming out later this year! You can pre-order it on the official website of Packt Publishing Ltd. or Amazon.

This will be an introductory book for folks who are already familiar with OpenCV, but now want to dive into the world of machine learning. The goal is to illustrate the fundamental machine learning concepts using practical, hands-on examples. As always, all source code will be available for free on GitHub.


The outline of the book is as follows:

  1. A Gentle Introduction to Machine Learning
  2. Working with Data Using OpenCV and Python
  3. First Steps in Supervised Learning
  4. Representing Data and Engineering Features
  5. Using Decision Trees to Make a Medical Diagnosis
  6. Detecting Pedestrians with Support Vector Machines
  7. Implementing a Spam Filter with Bayesian Learning
  8. Discovering Hidden Structures with Unsupervised Learning
  9. Using Deep Learning to Classify Handwritten Digits
  10. Combining Different Algorithms into an Ensemble
  11. Selecting the Right Model with Hyper-Parameter Tuning
  12. Wrapping Up

Stay tuned for more details!