This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques ? together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. Key Features Include: An introductory chapter on related mathematical toolsAll major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methodsA presentation of the physical reasoning, mathematical modeling and algorithmic implementation of each methodThe latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent modelingCase studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be appliedMATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of ´´boosting,´´ how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
O´Reilly´s bestselling book on Linux´s bash shell is at it again. Now that Linux is an established player both as a server and on the desktop Learning the bash Shell has been updated and refreshed to account for all the latest changes. Indeed, this third edition serves as the most valuable guide yet to the bash shell. As any good programmer knows, the first thing users of the Linux operating system come face to face with is the shell the UNIX term for a user interface to the system. In other words, it´s what lets you communicate with the computer via the keyboard and display. Mastering the bash shell might sound fairly simple but it isn´t. In truth, there are many complexities that need careful explanation, which is just what Learning the bash Shell provides. If you are new to shell programming, the book provides an excellent introduction, covering everything from the most basic to the most advanced features. And if you´ve been writing shell scripts for years, it offers a great way to find out what the new shell offers. Learning the bash Shell is also full of practical examples of shell commands and programs that will make everyday use of Linux that much easier. With this book, programmers will learn: - How to install bash as your login shell - The basics of interactive shell use, including UNIX file and directory structures, standard I/O, and background jobs - Command line editing, history substitution, and key bindings - How to customize your shell environment without programming - The nuts and bolts of basic shell programming, flow control structures, command-line options and typed variables - Process handling, from job control to processes, coroutines and subshells - Debugging techniques, such as trace and verbose modes - Techniques for implementing system-wide shell customization and features related to system security
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
Conventionally, physicians follow a cognitive decision making process, the appropriateness of which develops with experience and knowledge gained from literature, lectures etc. Having inadequate knowledge or experience may lead to misdiagnosis of diseases consequently affecting the patient physically, emotionally, financially and so on. Using data mining techniques, it is possible to develop accurate diagnostic models that can be used in clinical decision making. Adopting such models in clinical practice may ensure better decision making thereby decreasing the rate of misdiagnosis, minimising the uneasiness, pain and anxiety that is associated with a disease through early detection and treatment. In this thesis, (i) the performance of standard classification algorithms in CKD detection was explored (ii) a new hybrid approach to accurately diagnose CKD is presented and (iii) the application of distributed random forest algorithm for developing generalized model for CKD diagnosis was proposed.
Mobile learning refers to the acquisition of skills and knowledge through the use of mobile devices such as smartphones and tablets to enhance the overall learning experience. In this research, a systematic literature review of 38 articles was conducted to identify the current trends in mobile learning adoption between the periods 2012 to 2017. Of the 38 articles reviewed, 6 were extracted from EBSCO database, 8 from Emerald Insight, 9 from Science Direct, 8 from Springer and 7 from Scopus. The main aim of this study was to investigate the current trends in mobile learning technologies adoption by reviewing the methodologies used by researchers, theoretical frameworks, publication types, missing gaps, publication years and venues.
Breast cancer prediction is an open area of research. Breast cancer is a classification problem which can be solved by machine learning models like a decision tree, random forest, support vector machine, and many more models. Each machine learning model has its own merits and demerits. In breast cancer prediction we need to improve the accuracy of models, so we use here ensemble method which combines predictions of multiple models. An ensemble is a method to increase the prediction accuracy of the breast cancer. In this study, a new technique is introduced to genetic algorithm based weighted average ensemble method of classification data set which overcame the limitations of classical weighted average method. Genetic algorithm based weighted average method is used for the prediction of multiple models. The comparison between Particle swarm optimization (PSO), differential evolution (DE) and Genetic algorithm(GA) and it is concluded that the genetic algorithm outperforms for weighted average methods. One more comparison between classical ensemble method and GA based weighted average method and it is concluded that GA based weighted average methods outperforms.
First of all, I would like to thank almighty God,bestowing me the spirit of pursue research. It is obvious that writing a research report on any subject and any form involves enormous task. In this endeavor, many people have lend their helping hand for successful completion of the project. It is difficult to name each and every individual. But I acknowledge all the persons who have put their direct or indirect efforts in completing this task. I express my gratitude to Prof.A.Balasubramanian, Faculty & Dean of Science & Technology for his continuous support and encouragement in finishing my research work. I am also thankful to my technical guide Dr.Mukundaraj, MLRCC, Mysore and Deputy librarian Dr.Ramashesh for supporting by providing library facility during my research work. I would like to thank those who supporting to inculcate the habit of doing everything sincerely finishing that in a timely fashion and making time for maintaining health. I convey my gratitude to my sisters, specially mother for their bessings. I also want to aknowledge my relatives and other family members. I would like to express my deep gratitude to my husband B.Manjunatha and daughter Amulya M.