Exploring popular reinforcement learning environments in a beginner-friendly way, focusing on the Q-learning method to solve the 'Frozen Lake' environment.
This is a hands-on guide with Python example code that walks through the deployment of an ML-based search API using a simple 3-step approach. The article provides a deployment strategy applicable to most machine learning solutions, and the example code is available on GitHub.
This project implements a real-time face recognition system using OpenCV and the face_recognition library. The system detects faces from a video stream, recognizes previously seen faces, and displays metadata about each recognized face. It supports both Raspberry Pi cameras and USB webcams.
Comparing Clustering Algorithms
Following table will give a comparison (based on parameters, scalability and metric) of the clustering algorithms in scikit-learn.
Sr.No Algorithm Name Parameters Scalability Metric Used
1 K-Means No. of clusters Very large n_samples The distance between points.
2 Affinity Propagation Damping It’s not scalable with n_samples Graph Distance
3 Mean-Shift Bandwidth It’s not scalable with n_samples. The distance between points.
4 Spectral Clustering No.of clusters Medium level of scalability with n_samples. Small level of scalability with n_clusters. Graph Distance
5 Hierarchical Clustering Distance threshold or No.of clusters Large n_samples Large n_clusters The distance between points.
6 DBSCAN Size of neighborhood Very large n_samples and medium n_clusters. Nearest point distance
7 OPTICS Minimum cluster membership Very large n_samples and large n_clusters. The distance between points.
8 BIRCH Threshold, Branching factor Large n_samples Large n_clusters The Euclidean distance between points.
Following table will give a comparison (based on parameters, scalability and metric) of the clustering algorithms in scikit-learn.
Sr.No Algorithm Name Parameters Scalability Metric Used
1 K-Means No. of clusters Very large n_samples The distance between points.
2 Affinity Propagation Damping It’s not scalable with n_samples Graph Distance
3 Mean-Shift Bandwidth It’s not scalable with n_samples. The distance between points.
4 Spectral Clustering No.of clusters Medium level of scalability with n_samples. Small level of scalability with n_clusters. Graph Distance
5 Hierarchical Clustering Distance threshold or No.of clusters Large n_samples Large n_clusters The distance between points.
6 DBSCAN Size of neighborhood Very large n_samples and medium n_clusters. Nearest point distance
7 OPTICS Minimum cluster membership Very large n_samples and large n_clusters. The distance between points.
8 BIRCH Threshold, Branching factor Large n_samples Large n_clusters The Euclidean distance between points.
