DeepSeek is often encountered as a stream of model names, benchmark headlines, and claims of efficiency. The useful questions begin after the headline: What changed inside the model? How did training change behavior? And what evidence makes two results comparable?

Deep Dive into DeepSeek, Volume I: Models, Training, and Reasoning is a source-grounded guide to the architecture and training ideas behind the DeepSeek family. Beginning with reproducible requests and inference-cost fundamentals, it develops DeepSeekMoE, Multi-Head Latent Attention, and DeepSeek-V3; then follows pretraining, post-training, R1-Zero, DeepSeek-R1, mathematics and theorem proving, sparse long context, DeepSeek-V4, conditional memory, agent interfaces, and the DeepSeek Harness. Separate chapters cover coding, vision and multimodal generation, OCR, and document compression.

The book does more than summarize releases. It separates checkpoints from configurations, tokenizers, chat templates, hosted interfaces, and evaluation protocols. Worked examples, equations, diagrams, exercises, and source records connect public claims to mechanisms while distinguishing what is reported, derived, inspected, observed, or still unknown.
Written for ML practitioners, software engineers, researchers, and technically curious readers familiar with the basics of deep learning and transformers. No DeepSeek-specific background is required.
Volume II continues the story through serving, GPU kernels, distributed execution, storage, correctness, and measurement.