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The Language Models category spans books that explain, build, and critique systems that generate and understand human language, from classic statistical models to modern neural architectures like transformers and large language models. Titles range from accessible introductions and practical handbooks to advanced research texts, covering probabilistic foundations, deep learning, data curation, training and inference, scaling laws, evaluation, and tooling. Readers will find guidance on building, fine-tuning, and prompting models, along with case studies across search, coding, writing, education, and conversational agents. The category also foregrounds responsible AI—bias and fairness, safety, interpretability, security, privacy, and governance—and examines the social, economic, and legal implications of language technologies. It serves students, engineers, researchers, product leaders, and policymakers seeking both foundational knowledge and state-of-the-art insights.

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