Hands-On Unsupervised Learning with Python: Implement machine learning and deep learning models using Scikit-Learn,TensorFlow,and more
Authors: Giuseppe Bonaccorso
ISBN-10: 1789348277
ISBN-13: 9781789348279
Publication Date 出版日期: 2019-02-28
Print Length 页数: 386 pages
Publisher finelybook 出版社: Packt
Book Description
By finelybook
Discover the skill-sets required to implement various approaches to Machine Learning with Python
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.
What you will learn
Use cluster algorithms to identify and optimize natural groups of data
Explore advanced non-linear and hierarchical clustering in action
Soft label assignments for fuzzy c-means and Gaussian mixture models
Detect anomalies through density estimation
Perform principal component analysis using neural network models
Create unsupervised models using GANs
contents
1 Getting Started with Unsupervised Learning
2 Clustering Fundamentals
3 Advanced Clustering
4 Hierarchical Clustering in Action
5 Soft Clustering and Gaussian Mixture Models
6 Anomaly Detection
7 Dimensionality Reduction and Component Analysis
8 Unsupervised Neural Network Models
9 Generative Adversarial Networks and SOMs
