Table of Contents for
Hands-On Unsupervised Learning Using Python

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Hands-On Unsupervised Learning Using Python by Ankur A. Patel Published by O'Reilly Media, Inc., 2019
  1. Cover
  2. nav
  3. Hands-on Unsupervised Learning Using Python
  4. Hands-On Unsupervised Learning Using Python
  5. Preface
  6. I. Fundamentals of Unsupervised Learning
  7. 1. Unsupervised Learning in the Machine Learning Ecosystem
  8. 2. End-to-End Machine Learning Project
  9. II. Unsupervised Learning Using Scikit-Learn
  10. 3. Dimensionality Reduction
  11. 4. Anomaly Detection
  12. 5. Clustering
  13. 6. Group Segmentation
  14. III. Unsupervised Learning using TensorFlow and Keras
  15. 7. Autoencoders
  16. 8. Hands-On Autoencoder
  17. 9. Semi-Supervised Learning
  18. IV. Deep Unsupervised Learning using TensorFlow and Keras
  19. 10. Recommender Systems Using Restricted Boltzmann Machines
  20. 11. Feature Detection Using Deep Belief Networks
  21. 12. Generative Adversarial Networks
  22. 13. Time Series Clustering
  23. 14. Conclusion
  24. Index
  25. About the Author

Part III. Unsupervised Learning using TensorFlow and Keras

We just concluded the Scikit-Learn-based unsupervised learning portion of the book.

Now, we will move to neural network-based unsupervised learning.

In the next few chapters, we will introduce neural networks, including the popular frameworks to apply them such as TensorFlow and Keras.

In Chapter 7, we will use an autoencoder - a shallow neural network - to automatically perform feature engineering and feature selection.

In Chapter 8, we will apply autoencoders to a real world problem.

In Chapter 9, we will explore how to turn unsupervised learning problems into semi-supervised ones, leveraging the few labels we have to improve the precision and recall of a purely unsupervised model.

Once we are done reviewing shallow neural networks, we will move to deep neural networks in the last portion of the book.