- A dataset,
*X*, has a covariance matrix*C=diag(2, 1)*. What do you expect from PCA? - Considering the previous question, if
*X*is zero-centered and the ball,*B*, is empty, can we suppose that a threshold of_{0.5}(0, 0)*x = 0*(the first principal component) allows for horizontal discrimination? - The components extracted by PCA are statistically independent. Is this correct?
- A distribution with
*Kurt(X) = 5*is suitable for ICA. Is this correct? - What is the NNMF of a dataset,
*X*, containing the samples (*1, 2*) and (*0, -3*)? - A corpus of 10 documents is associated with a dictionary with 10 terms. We know that the fixed length of each document is 30 words. Is the dictionary over-complete?
- Kernel PCA is employed with a quadratic kernel. If the original dimensionality is 2, what is the dimensionality of the new space where the PCA is performed?

#### Hands-On Unsupervised Learning with Python

##### By :

#### Hands-On Unsupervised Learning with Python

##### By:

#### 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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