Hands-On Unsupervised Leaing with Python

by: Giuseppe Bonaccorso (Author)

Publisher: Packt Publishing

Publication Date: 2019/2/28

Language: English

Print Length: 386 pages

ISBN-10: 1789348277

ISBN-13: 9781789348279

Book Description

Discover the skill-sets required to implement various approaches to Machine Leaing with PythonKey FeaturesExplore unsupervised leaing with clustering, autoencoders, restricted Boltzmann machines, and more Build your own neural network models using mode Python libraries Practical examples show you how to implement different machine leaing and deep leaing techniques Book DescriptionUnsupervised leaing is about making use of raw, untagged data and applying leaing algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised leaing 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 leaing. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised leaing in both the machine leaing and deep leaing domains. You will explore various algorithms, techniques that are used to implement unsupervised leaing in real-world use cases. You will lea a variety of unsupervised leaing 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 leaed the art of unsupervised leaing for different real-world challenges.What you will leaUse 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 GANsWho this book is forThis book is intended for statisticians, data scientists, machine leaing developers, and deep leaing practitioners who want to build smart applications by implementing key building block unsupervised leaing, and master all the new techniques and algorithms offered in machine leaing and deep leaing using real-world examples. Some prior knowledge of machine leaing concepts and statistics is desirable.Table of ContentsGetting Started with Unsupervised LeaingClustering FundamentalsAdvanced ClusteringHierarchical Clustering in ActionSoft Clustering and Gaussian Mixture ModelsAnomaly DetectionDimensionality Reduction and Component AnalysisUnsupervised Neural Network ModelsGenerative Adversarial Networks and SOMs

About the Author

Discover the skill-sets required to implement various approaches to Machine Leaing with PythonKey FeaturesExplore unsupervised leaing with clustering, autoencoders, restricted Boltzmann machines, and more Build your own neural network models using mode Python libraries Practical examples show you how to implement different machine leaing and deep leaing techniques Book DescriptionUnsupervised leaing is about making use of raw, untagged data and applying leaing algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised leaing 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 leaing. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised leaing in both the machine leaing and deep leaing domains. You will explore various algorithms, techniques that are used to implement unsupervised leaing in real-world use cases. You will lea a variety of unsupervised leaing 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 leaed the art of unsupervised leaing for different real-world challenges.What you will leaUse 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 GANsWho this book is forThis book is intended for statisticians, data scientists, machine leaing developers, and deep leaing practitioners who want to build smart applications by implementing key building block unsupervised leaing, and master all the new techniques and algorithms offered in machine leaing and deep leaing using real-world examples. Some prior knowledge of machine leaing concepts and statistics is desirable.Table of ContentsGetting Started with Unsupervised LeaingClustering FundamentalsAdvanced ClusteringHierarchical Clustering in ActionSoft Clustering and Gaussian Mixture ModelsAnomaly DetectionDimensionality Reduction and Component AnalysisUnsupervised Neural Network ModelsGenerative Adversarial Networks and SOMs