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NAHIDUL HASAN

This is Nahid.I am Graduated from Computer Science and Engineering from Comilla University.Stay With me. "Of course you will that is gone from you, than that And something like that." Surah Anfal :70

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August 09, 2022

https://www.facebook.com/groups/csjobsinabroad/permalink/787915245787390/

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Forward and Viterbi Algo in HMM

July 03, 2023

Compare and Contrast HMM,RNN and CRF'S

August 11, 2023

*Hidden Markov Models (HMMs):* Definition: HMMs are generative probabilistic models that represent a sequence of observable events (outputs) as being generated by a sequence of hidden states. Applications: HMMs are often used in speech recognition, part-of-speech tagging, and bioinformatics, where the underlying process can be thought of as a sequence of states that emit observable symbols. Strengths: Simplicity: HMMs have a simple and interpretable structure, making them easy to understand and implement. Efficient Inference: The Viterbi algorithm can efficiently find the most likely sequence of hidden states given observations. Modeling Uncertainty: HMMs naturally account for uncertainty in state transitions and observations through probabilistic modeling. Limitations: Independence Assumption: HMMs assume that the current state depends only on the previous state, limiting their ability to capture long-range dependencies in data. Fixed Distribution: HMMs assume fixed distributions...

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