- The Manhattan distance is the same as the Minkowski distance with p=1; hence, we expect to observe a longer distance.
- No; the convergence speed is primarily influenced by the initial position of the centroids.
- Yes; k-means is designed to work with convex clusters, and its performances are poor with concave ones.
- It means that all clusters (except for a negligible percentage of samples), respectively, only contain samples belonging to the same class (that is, with the same true labels).
- It indicates a moderate/strong negative discrepancy between the true label distribution and the assignments. Such a value is a clear negative condition that cannot be accepted, because the vast majority of the samples have been assigned to the wrong clusters.
- No, because the adjusted Rand score is based on the ground truth (that is, the expected number of clusters is fixed).
- If all of...
Hands-On Unsupervised Learning with Python
By :
Hands-On Unsupervised Learning with Python
By:
Overview of this book
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.
This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.
By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
Related Content you might be interested in
Current Title:
Hands-On Unsupervised Learning with Python
Table of Contents (12 chapters)
Preface
Free Chapter
Getting Started with Unsupervised Learning
Clustering Fundamentals
Advanced Clustering
Hierarchical Clustering in Action
Soft Clustering and Gaussian Mixture Models
Anomaly Detection
Dimensionality Reduction and Component Analysis
Unsupervised Neural Network Models
Generative Adversarial Networks and SOMs
Assessments
Other Books You May Enjoy
Customer Reviews