Sources and licensing
This book draws its narrative content from fully open sources, supplemented by original writing. None of the sources below restrict commercial use — an earlier draft of this book used Michael Nielsen’s Neural Networks and Deep Learning for some early sessions, but it’s licensed CC BY-NC, so it was dropped in favor of Dive into Deep Learning throughout, keeping one consistent license across the whole book. Every page names which of these sources it used.
Dive into Deep Learning
Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. Dive into Deep Learning. https://d2l.ai
CC BY-SA 4.0. Primary source for every machine-learning session. Adapted material from this source is, per that license, shared here under the same terms.
Wikipedia
CC BY-SA 4.0. Conceptual and algorithmic depth for the bioinformatics sessions, and occasional historical notes elsewhere. Never assigned as reading directly — synthesized into this book’s own bounded pages instead (see the Welcome page for why).
EMBL-EBI Training
https://www.ebi.ac.uk/training/
CC BY 4.0. The practical, tool-use layer for databases and sequence alignment.
NCBI Bookshelf
https://www.ncbi.nlm.nih.gov/books/
Public domain (US government work). Used for the BLAST algorithm and database practicalities, including the BLAST® Help manual.
RCSB PDB-101
CC BY 4.0 — the PDB-101 educational/training tier specifically; general rcsb.org article text is separately copyrighted and is not used here.
A Byte of Python
Swaroop, C. H. A Byte of Python. https://python.swaroopch.com/
CC BY-SA 4.0, with commercial use explicitly permitted. Python fundamentals and its Object-Oriented Programming chapter.
What isn’t reused, and why
- Magnus Ekman’s Learning Deep Learning and Jonathan Pevsner’s Bioinformatics and Functional Genomics were this course’s previously assigned textbooks. Both are under conventional copyright. Nothing from either is reproduced here; any resemblance in topic ordering is because all of these books teach the same standard material, not because text was copied.
- Goodfellow, Bengio & Courville’s Deep Learning is free to read online (in the publisher’s own HTML viewer) but not licensed for reuse or redistribution — same treatment as the two textbooks above.
- Michael Nielsen’s Neural Networks and Deep Learning is CC BY- NC — freely reusable for non-commercial teaching, but dropped anyway so this book can carry one single, simpler license (CC BY-SA 4.0) throughout rather than mixed licensing across pages.
- OpenStax’s Introduction to Python Programming turned out, on checking, to also be CC BY-NC-SA — ruled out for the same reason.
- Python for Bioinformatics (BPB Publications): its GitHub repository is MIT-licensed, but that covers only the code samples — it’s the companion repo for a commercially sold, fully copyrighted book, not an open text itself.