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Pretraining is a book category centered on the theory and practice of training general-purpose machine learning models on large, diverse datasets before adapting them to specific tasks. Titles in this genre cover self- and unsupervised learning objectives (such as masked or contrastive learning), representation learning, transfer learning and fine-tuning, scaling laws and data curation, large-scale optimization and architecture design, compute and infrastructure considerations, evaluation and robustness, and ethical issues around data and deployment. The category spans text, vision, speech, code, and multimodal systems, including case studies and tooling for building, monitoring, and operationalizing foundation models.

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