A self-organizing map is a model that was proposed for the first time by Willshaw and Von Der Malsburg (in How Patterned Neural Connections Can Be Set Up by Self- Organization, Willshaw, D. J. and Von Der Malsburg, C., Proceedings of the Royal Society of London, B/194, N. 1117, 1976), with the goal of finding a way to describe different phenomena that happen in the brains of many animals. In fact, they observed that some areas of the brain can develop internally organized structures whose subcomponents are selectively receptive, with respect to specific input patterns (for example, some visual cortex areas are very responsive to vertical or horizontal bands). The central idea of an SOM can be synthesized by thinking about a clustering procedure aimed at finding out the low-level properties of a sample, thanks to its assignment to a cluster. The main practical...
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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
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