Beyeler Machine Learning for OpenCV

Intelligent image processing with Python
1. Auflage 2017
ISBN: 978-1-78398-029-1
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 382 Seiten

No detailed description available for "Machine Learning for OpenCV".

Autoren/Hrsg.

Beyeler, Michael

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Table of Contents - A Taste of Machine Learning
- Working with Data in OpenCV and Python
- First Steps in Supervised Learning
- Representing Data and Engineering Features
- Using Decision Trees to Make a Medical Diagnosis
- Detecting Pedestrians with Support Vector Machines
- Implementing a Spam Filter with Bayesian Learning
- Discovering Hidden Structures with Unsupervised Learning
- Using Deep Learning to Classify Handwritten Digits
- Combining Different Algorithms Into an Ensemble
- Selecting the Right Model with Hyperparameter Tuning
- Wrapping Up

Über Autor(innen)

Beyeler Michael:
Michael Beyeler is a postdoctoral fellow in neuroengineering and data science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye).His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland.