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Vibrational spectroscopy is a cornerstone in molecular analysis, offering detailed insights into chemical compositions and dynamics. Recent years have witnessed a paradigm shift with the integration of deep learning, which excels in automatically extracting intricate patterns from raw spectral data, bypassing traditional preprocessing steps. This synergy has significantly enhanced the precision and speed of applications ranging from material science to biomedical diagnostics. This book comprehensively explores the transformative impact of deep learning on vibrational spectroscopy modeling, emphasizing its superiority over traditional machine learning approaches. This book also provides an overview of the latest research and applications in vibrational spectroscopy over the past three years and offers insights into future directions for spectroscopic modeling in the face of big data challenges.