Book Image

Hands-On Unsupervised Learning with Python

By : Giuseppe Bonaccorso
Book Image

Hands-On Unsupervised Learning with Python

By: Giuseppe Bonaccorso

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)

Chapter 8

  1. No, they don't. Both the encoder and decoder must be functionally symmetric, but their internal structures can also be different.
  2. No; a part of the input information is lost during the transformation, while the remaining one is split between the code output Y and the autoencoder variables, which, along with the underlying model, encode all of the transformations.
  3. As min(sum(zi)) = 0 and min(sum(zi)) = 128, a sum equal to 36 can imply both sparseness (if the standard deviation is large) and a uniform distribution with small values (when the standard deviation is close to zero).
  4. As sum(zi) = 36, a std(zi) = 0.03 implies that the majority of values are centered around 0.28 (0.25 ÷ 0.31), the code can be considered dense.
  5. No; a Sanger network (as well as a Rubner-Tavan one) requires the input samples xi ∈ X.
  6. The components are extracted in descending order...