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Hidden Markov Models and AI Sequential Data Speech Recognition & NLP Applications VOL-2 (AI AND MATH NEW)
Format:
Hardcover
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
1.01 kg
1.01 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Artificial Intelligence today stands at the heart of technological transformation. From intelligent assistants and speech to text engines to autonomous systems, medical diagnostics, pattern recognition, fraud detection, cybersecurity, and natural language processing, AI technologies are increasingly grounded in the analysis of sequential data. Wherever events happen in time, wherever patterns unfold step by step, and wherever the present depends on the past, one family of mathematical models continues to play a foundational and irreplaceable role, Markov Models, especially Hidden Markov Models.This book, Hidden Markov Models and AI Sequential Data, Speech Recognition and NLP Applications, is a comprehensive, modern, and deeply practical introduction to Markov Models and their powerful applications in today’s AI landscape. Written for students, researchers, engineers, and professionals, this book bridges classical probabilistic modeling with cutting edge artificial intelligence, giving readers a complete understanding of how sequential data is modeled, processed, and leveraged by intelligent systems.Why This BookIn the age of deep learning and transformers, why should one still study Markov Models.Because sequential reasoning is not simply about prediction. It is about probability, hidden structure, temporal dependency, and the ability to explain why certain patterns occur. Hidden Markov Models remain essential in speech recognition, spelling correction, part of speech tagging, named entity recognition, chatbots, gesture and activity recognition, financial market analysis, bioinformatics, industrial automation, weather prediction, software verification, sensor fusion in robotics, and cybersecurity anomaly detection.Furthermore, while modern neural architectures such as recurrent neural networks, long short term memory networks, gated recurrent units, and transformers dominate many high level tasks, Markov Models remain the backbone of many mission critical, interpretable, and mathematically rigorous AI systems. They form the basis for probabilistic reasoning, language models, sequence alignment algorithms, speech decoding engines, and structure prediction.This book does not simply introduce the reader to Hidden Markov Models. It takes the reader on a structured journey from basic theory to real world deployment, ensuring mastery for academic and professional excellence.What This Book CoversThis book is divided into eight major parts, each systematically crafted to build a strong foundation while simultaneously guiding readers toward advanced applications.Part I Foundations of Markov Models and Sequential AIThe book begins with the fundamentals of sequential data, stochastic processes, and the essence of the Markov property. Readers are introduced to the history of Markov Models, their mathematical background, and why they remain crucial in modern AI applications. This part explains how sequential systems operate and prepares readers to understand the mathematics behind probabilistic reasoning.Part II Hidden Markov Models Theory, Mathematics, and AlgorithmsThis part forms the core of the book. It explains the inner workings of Hidden Markov Models, starting from state transition probabilities, emission probabilities, and initial distributions, and progressing toward the core algorithms that make Hidden Markov Models work.Forward Algorithm Used to evaluate the probability of an observed sequence.Backward Algorithm Complements the forward method for complete likelihood computation.Forward Backward Algorithm Used for smoothing and hidden state inference.
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U$S 52,63
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