Book Image

Hands-On Unsupervised Learning with Python

By : Giuseppe Bonaccorso
Book Image

Hands-On Unsupervised Learning with Python

By: Giuseppe Bonaccorso

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.
Table of Contents (12 chapters)

Chapter 5

  1. Hard clustering is based on fixed assignments; hence, a sample xi will always belong to a single cluster. Conversely, soft clustering returns a degree vector whose elements represent the membership level, with respect to each cluster (for example, (0.1, 0.7, 0.05, 0.15)).
  2. No; fuzzy c-means is an extension of k-means, and it's not particularly suitable for non-convex geometries. However, the soft assignments allow for evaluating the influence of neighboring clusters.
  3. The main assumption is that the dataset has been drawn from a distribution that can be efficiently approximated with the weighted sum of a number of Gaussian distributions.
  4. It means that the first model has a number of parameters that is the double of the second one.
  5. The second one, because it can achieve the same result with fewer parameters.
  6. Because we want to employ such a model for the auto-selection...