1. DSpace at My University

Search

Filters:

BROWSE

Has File(s)

Search Results

Results 1-10 of 340 (Search time: 0.07 seconds).
Item hits:
  • Book

  • Introduction to machine learning

    • Authors: Ethem Alpaydın (2010)

  • The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing class...
  • Book

  • Data Science

    • Authors: Kotu, Vijay; Bala Deshpande (2018)

  • Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data.
  • Book

  • Networking all-in-one for dummies

    • Authors: Doug Lowe (2016)

  • This fully-updated text will become your go-to guide for network development. This easy-to-understand guide gives you everything you need to dive into the network development game. Networking entails the connection of two or more computers, allowing them to share resources and exchange data. This is essential in the workplace, and has become highly popular in home settings as more and more people are connecting their home computers.
  • -

  • Artificial intelligence in IoT

    • Authors: Fadi Al-Turjman (2019)

  • This book provides an insight into IoT intelligence in terms of applications and algorithmic challenges. The book is dedicated to addressing the major challenges in realizing the artificial intelligence in IoT-based applications including challenges that vary from cost and energy efficiency to availability to service quality in multidisciplinary fashion.
  • -

  • Machine learning : a probabilistic perspective

    • Authors: Kevin P. Murphy (2012)

  • The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics.
  • Book

  • Introduction to machine learning

    • Authors: Ethem Alpaydin (2014)

  • Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods.
  • Book

  • Introduction to the theory of computation

    • Authors: Michael Sipser (2012)

  • The number one choice for today's computational theory course, this revision continues the book's well-known, approachable style with timely revisions, additional practice, and more memorable examples in key areas. A new first-of-its-kind theoretical treatment of deterministic context-free languages is ideal for a better understanding of parsing and LR grammars.
  • Book

  • Python Machine Learning

    • Authors: Sebastian Raschka (2015)

  • Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization * Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms * Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasets
  • Book

  • Operating system concepts

    • Authors: Abraham Silberschatz (2018)

  • It combines instruction on concepts with real-world applications so that students can understand the practical usage of the content. End-of-chapter problems, exercises, review questions, and programming exercises help to further reinforce important concepts. New interactive self-assessment problems are provided throughout the text to help students monitor their level of understanding and progress.