In this section, we are going to analyze two neural models (Sanger's and Rubner-Tavan's networks) that can perform principal component analysis (PCA) without the need of either eigendecomposing the covariance matrix or performing truncated SVD. They are both based on the concept of Hebbian learning (for further details, please refer to Dayan, P. and Abbott, L. F., Theoretical Neuroscience, The MIT Press, 2005 or Bonaccorso, G., Mastering Machine Learning Algorithms, Packt, 2018), which is one of the first mathematical theories about the dynamics of very simple neurons. Nevertheless, such concepts have very interesting implications, in particular in the field of component analysis. In order to better understand the dynamics of networks, it will be helpful to provide a quick overview of the basic model of a neuron. Let's...
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.
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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
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Customer Reviews