This is the code repository for Machine Learning for OpenCV – Advanced Methods and Deep Learning [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
In this video course , you will learn the following: Implement a Naïve Bayes classifier Discover hidden structures in your data using k-means clustering Implement k-means clustering and Expectation Maximization in OpenCV Implement a simple multi-layer perceptron in OpenCV Train and tweak neural networks • Build an ensemble classifier from decision trees in OpenCV • Combine different algorithms into a simple majority-vote classifier • Learn to tweak the hyperparameters of a model
- Implement a Naïve Bayes classifier
- Discover hidden structures in your data using k-means clustering
- Implement k-means clustering and Expectation Maximization in OpenCV
- Implement a simple multi-layer perceptron in OpenCV
- Train and tweak neural networks
- Build an ensemble classifier from decision trees in OpenCV
- Combine different algorithms into a simple majority-vote classifier
- Learn to tweak the hyperparameters of a model
To fully benefit from the coverage included in this course, you will need:
Basic working knowledge of computer vision and OpenCV
This course has the following software requirements:
You will need:
Anaconda 5.1
Jupyter Notebook
Python 3.6.4
OpenCV 3.1.0
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent