This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.
This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management.
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
The aim of this book is to help the readers understand the concepts, techniques, terminologies, and equations appearing in the existing books on engineering mathematics using MATLAB. Using MATLAB for computation would be otherwise time consuming, tedious and error-prone. The readers are recommended to have some basic knowledge of MATLAB.
This new edition provides an updated approach for students, engineers, and researchers to apply numerical methods for solving problems using MATLAB® This accessible book makes use of MATLAB® software to teach the fundamental concepts for applying numerical methods to solve practical engineering and/or science problems. It presents programs in a complete form so that readers can run them instantly with no programming skill, allowing them to focus on understanding the mathematical manipulation process and making interpretations of the results. Applied Numerical Methods Using MATLAB®, Second Edition begins with an introduction to MATLAB usage and computational errors, covering everything from input/output of data, to various kinds of computing errors, and on to parameter sharing and passing, and more. The system of linear equations is covered next, followed by a chapter on the interpolation by Lagrange polynomial. The next sections look at interpolation and curve fitting, nonlinear equations, numerical differentiation/integration, ordinary differential equations, and optimization. Numerous methods such as the Simpson, Euler, Heun, Runge-kutta, Golden Search, Nelder-Mead, and more are all covered in those chapters. The eighth chapter provides readers with matrices and Eigenvalues and Eigenvectors. The book finishes with a complete overview of differential equations. Provides examples and problems of solving electronic circuits and neural networks Includes new sections on adaptive filters, recursive least-squares estimation, Bairstow's method for a polynomial equation, and more Explains Mixed Integer Linear Programing (MILP) and DOA (Direction of Arrival) estimation with eigenvectors Aimed at students who do not like and/or do not have time to derive and prove mathematical results Applied Numerical Methods Using MATLAB®, Second Edition is an excellent text for students who wish to develop their problem-solving capability without being involved in details about the MATLAB codes. It will also be useful to those who want to delve deeper into understanding underlying algorithms and equations.
This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.
This book discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management.
Provides practical examples of circuit design and analysis using PSpice, MATLAB, and the Smith Chart This book presents the three technologies used to deal with electronic circuits: MATLAB, PSpice, and Smith chart. It gives students, researchers, and practicing engineers the necessary design and modelling tools for validating electronic design concepts involving bipolar junction transistors (BJTs), field-effect transistors (FET), OP Amp circuits, and analog filters. Electronic Circuits with MATLAB®, PSpice®, and Smith Chart presents analytical solutions with the results of MATLAB analysis and PSpice simulation. This gives the reader information about the state of the art and confidence in the legitimacy of the solution, as long as the solutions obtained by using the two software tools agree with each other. For representative examples of impedance matching and filter design, the solution using MATLAB and Smith chart (Smith V4.1) are presented for comparison and crosscheck. This approach is expected to give the reader confidence in, and a deeper understanding of, the solution. In addition, this text: Increases the reader's understanding of the underlying processes and related equations for the design and analysis of circuits Provides a stepping stone to RF (radio frequency) circuit design by demonstrating how MATLAB can be used for the design and implementation of microstrip filters Features two chapters dedicated to the application of Smith charts and two-port network theory Electronic Circuits with MATLAB®, PSpice®, and Smith Chart will be of great benefit to practicing engineers and graduate students interested in circuit theory and RF circuits.
The international market is very competitive for high-tech manufacturers to day. Achieving competitive quality and reliability for products requires leader ship from the top, good management practices, effective and efficient operation and maintenance systems, and use of appropriate up-to-date engineering de sign tools and methods. Furthermore, manufacturing yield and reliability are interrelated. Manufacturing yield depends on the number of defects found dur ing both the manufacturing process and the warranty period, which in turn determines the reliability. the production of microelectronics has evolved into Since the early 1970's, one of the world's largest manufacturing industries. As a result, an important agenda is the study of reliability issues in fabricating microelectronic products and consequently the systems that employ these products, particularly, the new generation of microelectronics. Such an agenda should include: • the economic impact of employing the microelectronics fabricated by in dustry, • a study of the relationship between reliability and yield, • the progression toward miniaturization and higher reliability, and • the correctness and complexity of new system designs, which include a very significant portion of software.
Atomic Information Technology revaluates current conceptions of the information technology aspects of the nuclear industry. Economic and safety research in the nuclear energy sector are explored, considering statistical methods which incorporate Monte-Carlo simulations for practical applications. Divided into three sections, Atomic Information Technology covers: Atomic economics and management, Atomic safety and reliability, and Atomic safeguarding and security. Either as a standalone volume or as a companion to conventional nuclear safety and reliability books, Atomic Information Technology acts as a concise and thorough reference on statistical assessment technology in the nuclear industry. Students and industry professionals alike will find this a key tool in expanding and updating their understanding of this industry and the applications of information technology within it.
This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
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