Book Image

Machine Learning for OpenCV - Advanced Methods and Deep Learning [Video]

By : Michael Beyeler
Book Image

Machine Learning for OpenCV - Advanced Methods and Deep Learning [Video]

By: Michael Beyeler

Overview of this book

<p>Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains.</p> <p>This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems.</p> <p>All the code and supporting files for this course are available on Github at <a style="color: #fa8d11;" href="https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Advanced-Methods-and-Deep-Learning" target="blank">https://github.com/PacktPublishing/Machine-Learning-for-OpenCV-Advanced-Methods-and-Deep-Learning</a></p> <p>The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems.</p> <h2>Style and Approach</h2> <p>This course walks you through the key elements of OpenCV and its powerful Machine Learning classes while demonstrating how to get to grips with a range of models.</p>
Table of Contents (5 chapters)
Chapter 5
Selecting the Right Model with Hyperparameter Tuning
Content Locked
Section 1
Evaluating a Model
Model evaluation strategies come in many different forms and shapes. In principle, model evaluation is simple – after training a model on some data, we can estimate its effectiveness by comparing model predictions to some ground truth values. - Evaluate a model the wrong way - Evaluate a model in the right way - Select the best model