Machine Learning

Author: Peter Flach
Publisher: Cambridge University Press
ISBN: 1107096391
Size: 64.51 MB
Format: PDF, Mobi
View: 2938
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

Machine Learning

Author: Peter Flach
Publisher: Cambridge University Press
ISBN: 1139577174
Size: 21.70 MB
Format: PDF, ePub, Mobi
View: 7365
As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

Standard Handbook For Electrical Engineers Seventeenth Edition

Author: Surya Santoso
Publisher: McGraw Hill Professional
ISBN: 1259642593
Size: 35.82 MB
Format: PDF, ePub
View: 3177
Up-to-date coverage of every facet of electric power in a single volume This fully revised, industry-standard resource offers practical details on every aspect of electric power engineering. The book contains in-depth discussions from more than 100 internationally recognized experts. Generation, transmission, distribution, operation, system protection, and switchgear are thoroughly explained. Standard Handbook for Electrical Engineers, Seventeenth Edition, features brand-new sections on measurement and instrumentation, interconnected power grids, smart grids and microgrids, wind power, solar and photovoltaic power generation, electric machines and transformers, power system analysis, operations, stability and protection, and the electricity market. Coverage includes: •Units, symbols, constants, definitions, and conversion factors •Measurement and instrumentation •Properties of materials •Interconnected power grids •AC and DC power transmission •Power distribution •Smart grids and microgrids •Wind power generation •Solar power generation and energy storage •Substations and switch gear •Power transformers, generators, motors, and drives •Power electronics •Power system analysis, operations, stability, and protection •Electricity markets •Power quality and reliability •Lightning and overvoltage protection •Computer applications in the electric power industry •Standards in electrotechnology, telecommunications, and IT

The Realities Of Reality Part Ii Making Sense Of Why Modern Science Advances Volume 1

Author: Fritz Dufour, MBA, DESS
Publisher: Fritz Dufour
Size: 31.84 MB
Format: PDF, Docs
View: 3300
This Volume 1 of Part II considers the factors that make science progress. It lays out the differences between normal science and pseudoscience by showing the importance of the scientific method in the advancement of science. It introduces the concept of Truth in science by raising the point that even though truth is based on the scientific method, can science be true? Can it depict reality? The author focuses on modern science, which, he thinks, was born thanks to the Scientific Revolution which started with Galileo Galilei and led to the Industrial Revolution. The impacts of the latter is analyzed in light modernism, modernization, and modernity, all three linked to scientific progress. The book also talks about the Newtonian scientific leap – by analyzing particularly the then social and political fabrics of England – and Albert Einstein by showing how he changed history. According to the author, our very physical world can help us understand scientific progress. So, he explains, among other things, the structure of atoms and molecules, the role of physics in the understanding of our universe, Quantum Mechanics, and the importance of Higgs-Boson. On the other hand, the book is a stunning revelation of how important information is to scientific progress. To make his point, the author, first, talks about John Vincent Atanasoff as the Father of computer thanks to the invention of his ABC computer and then, Alan Turing as the Father of modern computer thanks to his Turing Test and his views on Artificial Intelligence. Both men played a momentous role in the Digital Revolution and in the Information Age, according to the book. Finally, the author talks about nanotechnology, which explores the world of small, meaning at the atomic and the molecular levels and is an inescapable tool in the molecular biology revolution which, itself, is an important factor in scientific progress and in transhumanism or human enhancement defined as the ideology according to which man can surpass his present state by improving his genetic material.

Data Mining

Author: Krzysztof J. Cios
Publisher: Springer Science & Business Media
ISBN: 0387367950
Size: 29.69 MB
Format: PDF, Docs
View: 3352
This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.

Machine Learning For Dummies

Author: John Paul Mueller
Publisher: John Wiley & Sons
ISBN: 1119245516
Size: 55.22 MB
Format: PDF, Mobi
View: 2187
Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Deep Learning For Dummies

Author: John Paul Mueller
Publisher: For Dummies
ISBN: 1119543045
Size: 71.91 MB
Format: PDF, Kindle
View: 1932
Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Signal Processing And Machine Learning For Biomedical Big Data

Author: Ervin Sejdic
Publisher: CRC Press
ISBN: 1351061216
Size: 44.82 MB
Format: PDF, Kindle
View: 1489
This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will describe signal processing, machine learning, and parallel computing strategies to revolutionize the world of medical analytics and diagnosis as presented by world class researchers and experts in this important field. The chapters will desribe tools that can be used by biomedical and clinical practitioners as well as industry professionals. It will give signal processing researchers a glimpse into the issues faced with Big Medical Data.

Supervised And Unsupervised Learning For Data Science

Author: Michael W. Berry
Publisher: Springer Nature
ISBN: 3030224759
Size: 15.56 MB
Format: PDF
View: 1548
This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.