Sequence Sense: Modeling Time, Motion, and Meaning

Understanding the world requires understanding order. Sequence Sense is a deep learning book devoted to modeling sequences—how machines learn from time, motion, and progression to derive meaning.
The writing explores sequential data across domains: language, speech, video, sensors, and behavior. Readers learn how models capture dependency, causality, and rhythm—turning streams of data into structured understanding.
Rather than isolating techniques, the book compares approaches. It examines recurrent models, temporal convolution, and attention-based systems, showing how each represents sequence differently. Emphasis is placed on when sequence matters, how memory is encoded, and why context across time changes interpretation.
The tone is analytical and grounded, building intuition without overwhelming formalism. Language remains clear and systematic, helping readers reason about temporal modeling choices and trade-offs.
Sequence Sense moves through sequence representation, temporal dynamics, alignment, prediction, and evaluation—revealing how time-aware models unlock deeper insight.
Key themes explored include:
• Temporal and sequential modeling
• Memory and dependency
• Time-aware representations
• Motion and progression
• Meaning through order
Sequence Sense is for practitioners working with evolving data—offering guidance to build models that understand not just what happens, but when and why.