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Deep Learning: Making It Learnable v1.0

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@hshakeri hshakeri released this 16 Jul 14:00
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v1.0
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Deep Learning: Making It Learnable — v1.0

Version 1.0 is the first stable, citable edition of Heman Shakeri's UVA DS 6050
course companion.

What this edition contains

  • The complete twenty-chapter arc, from linear models through multimodal learning,
    with two bridge interludes, an epilogue, and four just-in-time appendices.
  • The derivation-first test-time memory extension: attention as local-constant kernel
    regression, the KV cache as its retained dataset, and linear/delta state as distinct
    solver contracts. The epilogue closes with a carefully scoped memory-to-planning
    frontier.
  • A pinned Chapter 1–9 corpus for the Chapter 10/14 language-model rematch, seeded
    CPU experiments, paired HTML/PDF freezes, and explicit evaluation disclosures.
  • A consistent presentation covenant: experiments, derivations, assertions, and
    numerical audits expose their code; pure concept diagrams keep executable drawing
    source in the repository without printing pages of coordinates.
  • Stable citation metadata in CITATION.cff and HTML revision notes for migrating
    pre-v1.0 page references.

Release artifact

The attached Deep-Learning--Making-It-Learnable.pdf is the verified 498-page edition.

Suggested citation: Shakeri, Heman. 2026. Deep Learning: Making It Learnable.
Version 1.0. https://shakeri-lab.github.io/dl-book/.

Text and figures are licensed CC BY-NC-SA 4.0. Code is MIT licensed; third-party
notices and asset provenance remain in the tagged source.

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