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Hands-On Markov Models with Python
HiddenMarkovModel(HMM)isastatisticalmodelbasedontheMarkovchainconcept.Hands-OnMarkovModelswithPythonhelpsyougettogripswithHMMsanddifferentinferencealgorithmsbyworkingonreal-worldproblems.Thehands-onexamplesexploredinthebookhelpyousimplifytheprocessflowinmachinelearningbyusingMarkovmodelconcepts,therebymakingitaccessibletoeveryone.Onceyou’vecoveredthebasicconceptsofMarkovchains,you’llgetinsightsintoMarkovprocesses,models,andtypeswiththehelpofpracticalexamples.Aftergraspingthesefundamentals,you’llmoveontolearningaboutthedifferentalgorithmsusedininferencesandapplyingtheminstateandparameterinference.Inadditiontothis,you’llexploretheBayesianapproachofinferenceandlearnhowtoapplyitinHMMs.Infurtherchapters,you’lldiscoverhowtouseHMMsintimeseriesanalysisandnaturallanguageprocessing(NLP)usingPython.You’llalsolearntoapplyHMMtoimageprocessingusing2D-HMMtosegmentimages.Finally,you’llunderstandhowtoapplyHMMforreinforcementlearning(RL)withthehelpofQ-Learning,andusethistechniqueforsingle-stockandmulti-stockalgorithmictrading.Bytheendofthisbook,youwillhavegraspedhowtobuildyourownMarkovandhiddenMarkovmodelsoncomplexdatasetsinordertoapplythemtoprojects.
目錄(128章)
倒序
- 封面
- Title Page
- Copyright and Credits
- Hands-On Markov Models with Python
- Packt Upsell
- Why subscribe?
- packt.com
- Contributors
- About the authors
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- 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 chains
- Exponential distributions
- Poisson process
- Continuous-time Markov chain example
- Continuous-time Markov chain
- Summary
- 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
- State Inference - Predicting the States
- State inference in HMM
- Dynamic programming
- Forward algorithm
- Computing the conditional distribution of the hidden state given the observations
- Backward algorithm
- Forward-backward algorithm (smoothing)
- The Viterbi algorithm
- Summary
- 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
- Parameter Inference Using the Bayesian Approach
- Bayesian learning
- Selecting the priors
- Intractability
- Bayesian learning in HMM
- Approximating required integrals
- Sampling methods
- Laplace approximations
- Stolke and Omohundro's method
- Variational methods
- Code
- Summary
- Time Series Predicting
- Stock price prediction using HMM
- Collecting stock price data
- Features for stock price prediction
- Predicting price using HMM
- Summary
- 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 SpeechRecognition
- Speech recognition from audio files
- Speech recognition using the microphone
- Summary
- 2D HMM for Image Processing
- Recap of 1D HMM
- 2D HMMs
- Algorithm
- Assumptions for the 2D HMM model
- Parameter estimation using EM
- Summary
- 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
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