The first algorithm that we will propose is a variation of k-means that's based on soft assignments. The name Fuzzy c-means derives from the concept of a fuzzy set, which is an extension of classical binary sets (that is, in this case, a sample can belong to a single cluster) to sets based on the superimposition of different subsets representing different regions of the whole set. For example, a set based on the age of some users can have the degrees young, adult, and senior, associated with three different (and partially overlapping) age ranges: 18-35, 28-60, and >50. So, for example, a 30-year-old user is both young and adult, to different degrees (and, indeed, is a borderline user, considering the boundaries). For further details about these kinds of sets and all of the related operations, I suggest the book Concepts and Fuzzy Logic, Belohlavek R., Klir...
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Table Of Contents
Hands-On Unsupervised Learning with Python
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Hands-On Unsupervised Learning with Python
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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)
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
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