Описание: Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.
Автор: Qidwai Название: Digital image processign an algorit ISBN: 1138115185 ISBN-13(EAN): 9781138115187 Издательство: Taylor&Francis Рейтинг: Цена: 11329.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Avoiding heavy mathematics and lengthy programming details, Digital Image Processing: An Algorithmic Approach with MATLAB(R) presents an easy methodology for learning the fundamentals of image processing. The book applies the algorithms using MATLAB(R), without bogging down students with syntactical and debugging issues.
One chapter can typically be completed per week, with each chapter divided into three sections. The first section presents theoretical topics in a very simple and basic style with generic language and mathematics. The second section explains the theoretical concepts using flowcharts to streamline the concepts and to form a foundation for students to code in any programming language. The final section supplies MATLAB codes for reproducing the figures presented in the chapter. Programming-based exercises at the end of each chapter facilitate the learning of underlying concepts through practice.
This textbook equips undergraduate students in computer engineering and science with an essential understanding of digital image processing. It will also help them comprehend more advanced topics and sophisticated mathematical material in later courses. A color insert is included in the text while various instructor resources are available on the author's website.
Описание: Do you want to learn how to design and master different machine learning algorithms quickly and easily?
If you want to be a machine learning expert, you do have to develop a sound understanding of all the nitty-gritty of this area and there's no other way around it.
But relax, this book is here to rescue you by simplifying and providing a working definition of machine learning technology.
The concepts of machine learning and machine learning algorithms can appear daunting and complex to most computer programming beginners.
Most of the information out there on various machine learning constitutes a description of a few pages, and yes, it is difficult to find time and energy to deal with such widespread detail.
However, it is important to master the concepts of machine learning technology and learn how researchers are breaking the boundaries of data science to mimic human intelligence in machines using various machine learning algorithms.
Some of the highlights of the book include:
- Master the concepts of artificial intelligence technology
- Learn how artificial intelligence technology is being applied in some of the most important industrial domains
- Learn the basic concepts and the definition of machine learning.
- Overview of different types of machine learning algorithms along with the relationship between machine learning and Artificial Intelligence technology.
- A thorough understanding of the concept of the "Statistical Learning" framework of machine learning has been provided.
- Deep dive into a variety of supervised and unsupervised learning algorithms explained in exquisite detail.
- Select algorithms have been explained with required mathematical equations for application in real life.
- Learn how the "Artificial Neural Networks" or (ANN) have been developed with inspiration from the structure of the human brain.
- Learn the 9 stages to create a data pipeline to build your own machine learning model.
- Learn the basics of Python programming language and some of the key features that render it as the language of choice for coding beginners and advanced software programmers alike.
- An overview of various renowned machine learning libraries such as "Scikit-Learn", "NumPy", "SciPy", "IPython", and "Pandas" among others.
This book is filled with real-life examples to help you understand the nitty-gritty of the concepts and names and descriptions of multiple tools that you can further explore and selectively implement to make sound choices for the development of a desired machine learning model.
Finally, as an added bonus you will learn some Python tips and tricks to take your machine learning programming game to the next level.
Remember knowledge is power and with the great power you will gather from this book, you will be armed to make sound personal and professional technological choices.
Your Python programming skillset will improve drastically and you will be poised to develop your very own machine learning model
Описание: Do you want to learn how to design and master different machine learning algorithms quickly and easily?
If you want to be a machine learning expert, you do have to develop a sound understanding of all the nitty-gritty of this area and there's no other way around it.
But relax, this book is here to rescue you by simplifying and providing a working definition of machine learning technology.
The concepts of machine learning and machine learning algorithms can appear daunting and complex to most computer programming beginners.
Most of the information out there on various machine learning constitutes a description of a few pages, and yes, it is difficult to find time and energy to deal with such widespread detail.
However, it is important to master the concepts of machine learning technology and learn how researchers are breaking the boundaries of data science to mimic human intelligence in machines using various machine learning algorithms.
Some of the highlights of the book include:
- Master the concepts of artificial intelligence technology
- Learn how artificial intelligence technology is being applied in some of the most important industrial domains
- Learn the basic concepts and the definition of machine learning.
- Overview of different types of machine learning algorithms along with the relationship between machine learning and Artificial Intelligence technology.
- A thorough understanding of the concept of the "Statistical Learning" framework of machine learning has been provided.
- Deep dive into a variety of supervised and unsupervised learning algorithms explained in exquisite detail.
- Select algorithms have been explained with required mathematical equations for application in real life.
- Learn how the "Artificial Neural Networks" or (ANN) have been developed with inspiration from the structure of the human brain.
- Learn the 9 stages to create a data pipeline to build your own machine learning model.
- Learn the basics of Python programming language and some of the key features that render it as the language of choice for coding beginners and advanced software programmers alike.
- An overview of various renowned machine learning libraries such as "Scikit-Learn", "NumPy", "SciPy", "IPython", and "Pandas" among others.
This book is filled with real-life examples to help you understand the nitty-gritty of the concepts and names and descriptions of multiple tools that you can further explore and selectively implement to make sound choices for the development of a desired machine learning model.
Finally, as an added bonus you will learn some Python tips and tricks to take your machine learning programming game to the next level.
Remember knowledge is power and with the great power you will gather from this book, you will be armed to make sound personal and professional technological choices.
Your Python programming skillset will improve drastically and you will be poised to develop your very own machine learning model
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