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Hidden Markov Model (HMM)

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Hidden Markov Model (HMM)  VIDEO LINK:  https://youtu.be/YIGCWNG8BIA A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. Video Chapters: HMM in Artificial Intelligence 00:00 Introduction 00:31 Statistical Model 00:54 HMM Examples 02:30 HMM 03:10 HMM Components 05:23 Viterbi Algorithm 06:23 HMM Applications 06:38 HMM Problems 07:28 HMM in Handwriting Recognition 11:20 Conclusion  HMM COMPONENTS A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. An HMM consists of states, observations, transition probabilities, emission probabilities, and initial probabilities. It is commonly used in a...

GWO In Hindi || Step-By-Step|| ~xRay Pixy

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Learn Grey Wolf Optimizer Step-by-Step using examples in Hindi. Video Chapters: Grey Wolf Optimizer 00:00 Introduction 00:47 Topics Covered 01:28 Grey Wolf Real-life Behavior 04:35 GWO Simulation 09:36 GWO Step-By-Step 16:50  GWO Applications 17:07 GWO Advantages 17:22 GWO Disadvantages 17:29 Conclusion Grey wolves, in the wild, have a natural ability to locate prey and encircle it during a hunt. This process is led by the alpha wolf , with occasional help from the beta and delta wolves . The remaining wolves (omegas) follow the leaders' guidance. In optimization problems, however, the location of the optimal solution (the "prey") is unknown. To mimic this behavior in the Grey Wolf Optimizer (GWO), we make some assumptions: Alpha, beta, and delta are considered the top three best solutions found so far. These three "leader wolves" guide the movement of all other solutions (search agents or omegas). Grey Wolf Optimizer (GWO) is directly inspired by the social ...
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