In Chapter 7, Dimensionality Reduction And Component Analysis, we discussed some common methods that can be employed to reduce the dimensionality of a dataset, given its peculiar statistical properties (for example, the covariance matrix). However, when complexity increases, even kernel principal component analysis (kernel PCA) might be unable to find a suitable lower-dimensional representation. In other words, the loss of information can overcome a threshold that guarantees the possibility of rebuilding the samples effectively. Autoencoders are models that exploit the extreme non-linearity of neural networks, in order to find low-dimensional representations of a given dataset. In particular, let's assume that X is a set of samples drawn from a data-generating process, pdata(x). For simplicity, we will consider xi ∈ ℜn, but there are no restrictions...
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