Compare and Contrast HMM,RNN and CRF'S
*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