90折
Machine Learning and its Applications
商品資訊
ISBN13：9781138328228
出版社：CRC Pr I Llc
作者：Peter (University of Southern Queensland Wlodarczak Toowoomba Australia)
出版日：2019/11/04
裝訂／頁數：精裝／188頁
規格：15.6cm*23.5cm (高/寬)
定價
：NT$ 10725 元優惠價
：90 折 9653 元
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商品簡介
In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g.
deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge.
This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations.
The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general. This book is meant to be an introduction into machine learning.
It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning.
Finally, it also intends to be a reference book. Key Features:Describes real world problems that can be solved using Machine LearningProvides methods for directly applying Machine Learning techniques to concrete real world problemsDemonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R
deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge.
This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations.
The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general. This book is meant to be an introduction into machine learning.
It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning.
Finally, it also intends to be a reference book. Key Features:Describes real world problems that can be solved using Machine LearningProvides methods for directly applying Machine Learning techniques to concrete real world problemsDemonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R
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