Language, Learned: Transformers and the New Literacy

Language is no longer only written—it is learned. Language, Learned is a deep learning book devoted to understanding how transformer architectures have redefined literacy for machines and reshaped how humans interact with language at scale.
The writing traces the evolution from rule-based language processing to representation-driven learning. Readers explore how transformers model context, meaning, and intent through attention rather than fixed grammar or handcrafted features. Language here is treated as pattern, probability, and relationship.
Rather than focusing on implementation alone, the book builds conceptual clarity. It explains why scale matters, how pretraining changes capability, and how fine-tuning adapts general language understanding to specific tasks. Each chapter connects architectural choices to real-world outcomes in translation, summarization, search, and dialogue.
The tone is explanatory and forward-looking, suitable for learners, practitioners, and leaders. Language remains precise yet accessible, emphasizing intuition behind the math.
Language, Learned moves through embeddings, self-attention, pretraining paradigms, alignment challenges, and societal impact—framing transformers as a new form of literacy.
Key themes explored include:
• Transformers and language modeling
• Context and meaning at scale
• Pretraining and adaptation
• Language as representation
• The future of human–AI communication
Language, Learned is for readers navigating a world where machines read, write, and reason—offering insight into the architecture behind modern language intelligence.