가야대학교 분성도서관

상단 글로벌/추가 메뉴

회원 로그인


자료검색

자료검색

상세정보

부가기능

Hands-On Markov Models with Python : Implement Probabilistic Models for Learning Complex Data Sequences Using the Python Ecosystem. [electronic resource]

상세 프로파일

상세정보
자료유형E-Book
개인저자Ankan, Ankur.
Panda, Abinash.
서명/저자사항Hands-On Markov Models with Python :Implement Probabilistic Models for Learning Complex Data Sequences Using the Python Ecosystem.[electronic resource]
발행사항Birmingham : Packt Publishing Ltd, 2018.
형태사항1 online resource (172 pages)
소장본 주기Added to collection customer.56279.3
ISBN9781788629331
1788629337
내용주기Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to the Markov Process; Random variables; Random processes; Markov processes; Installing Python and packages; Installation on Windows; Installation on Linux; Markov chains or discrete-time Markov processes; Parameterization of Markov chains; Properties of Markov chains; Reducibility; Periodicity; Transience and recurrence; Mean recurrence time; Expected number of visits; Absorbing states; Ergodicity; Steady-state analysis and limiting distributions.
Continuous-time Markov chainsExponential distributions; Poisson process; Continuous-time Markov chain example; Continuous-time Markov chain; Summary; Chapter 2: Hidden Markov Models; Markov models; State space models; The HMM; Parameterization of HMM; Generating an observation sequence; Installing Python packages; Evaluation of an HMM; Extensions of HMM; Factorial HMMs; Tree-structured HMM; Summary; Chapter 3: State Inference -- Predicting the States; State inference in HMM; Dynamic programming; Forward algorithm.
Computing the conditional distribution of the hidden state given the observationsBackward algorithm; Forward-backward algorithm (smoothing); The Viterbi algorithm; Summary; Chapter 3: Parameter Learning Using Maximum Likelihood; Maximum likelihood learning; MLE in a coin toss; MLE for normal distributions; MLE for HMMs; Supervised learning; Code; Unsupervised learning; Viterbi learning algorithm; The Baum-Welch algorithm (expectation maximization); Code; Summary; Chapter 4: Parameter Inference Using the Bayesian Approach; Bayesian learning; Selecting the priors; Intractability.
Bayesian learning in HMMApproximating required integrals; Sampling methods; Laplace approximations; Stolke and Omohundro's method; Variational methods; Code; Summary; Chapter 5: Time Series Predicting; Stock price prediction using HMM; Collecting stock price data; Features for stock price prediction; Predicting price using HMM; Summary; Chapter 6: Natural Language Processing; Part-of-speech tagging; Code; Getting data; Exploring the data; Finding the most frequent tag; Evaluating model accuracy; An HMM-based tagger; Speech recognition; Python packages for speech recognition.
Basics of SpeechRecognitionSpeech recognition from audio files; Speech recognition using the microphone; Summary; Chapter 7: 2D HMM for Image Processing; Recap of 1D HMM; 2D HMMs; Algorithm; Assumptions for the 2D HMM model; Parameter estimation using EM; Summary; Chapter 8: Markov Decision Process; Reinforcement learning; Reward hypothesis; State of the environment and the agent; Components of an agent; The Markov reward process; Bellman equation; MDP; Code example; Summary; Other Books You May Enjoy; Index.
요약This book will help you become familiar with HMMs and different inference algorithms by working on real-world problems. You will start with an introduction to the basic concepts of Markov chains, Markov processes and then delve deeper into understanding hidden Markov models and its types using practical examples.
일반주제명Python.
Machine learning.
Machine learning.
언어영어
기타형태 저록Print version:Ankan, Ankur.Hands-On Markov Models with Python : Implement Probabilistic Models for Learning Complex Data Sequences Using the Python Ecosystem.Birmingham : Packt Publishing Ltd, 짤20189781788625449
대출바로가기http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1904975

소장정보

  • 소장정보

인쇄 인쇄

메세지가 없습니다
No. 등록번호 청구기호 소장처 도서상태 반납예정일 예약 서비스 매체정보
1 WE00016345 005.133 가야대학교/전자책서버(컴퓨터서버)/ 대출가능 인쇄 이미지  

서평

  • 서평

태그

  • 태그

나의 태그

나의 태그 (0)

모든 이용자 태그

모든 이용자 태그 (0) 태그 목록형 보기 태그 구름형 보기
 

퀵메뉴

대출현황/연장
예약현황조회/취소
자료구입신청
상호대차
FAQ
교외접속
사서에게 물어보세요
메뉴추가
quickBottom

카피라이터

  • 개인정보보호방침
  • 이메일무단수집거부

김해캠퍼스 | 621-748 | 경남 김해시 삼계로 208 | TEL:055-330-1033 | FAX:055-330-1032
			Copyright 2012 by kaya university Bunsung library All rights reserved.