Panel - Tips for Writing an Effective AI and Deep Learning Book
summary
Panel reports practical author choices: publishing route matters (self-publish boosts revenue, a major publisher supplies reviewers and marketing), many projects run two years not two months, code-first books benefit from Jupyter notebooks as canonical source to keep examples runnable, PyTorch wins sometimes to force learning while writing (choose PyTorch to learn while writing), coauthor count ideally two, mornings win for focused drafting, avoid forward-referencing, track figures and code in a simple log, aim a single, concrete target audience.
AI-written summary · about
chapters (20)
- 0:00Introductions and backgrounds
- 2:10Why authors write books
- 9:58Self-publishing vs traditional publishing
- 14:35Motivations behind writing AI books
- 17:05Finding a publisher for your book
- 20:55What makes a strong book proposal
- 23:06Co-authoring a book
- 26:18Writing during the pandemic
- 29:05Evolving target audiences
- 31:14Defining your book’s audience
- 34:01Daily writing routines
- 35:48Revising and improving content
- 37:34Structuring information effectively
- 39:21Balancing code, text, and figures
- 41:26Condensing and targeting content
- 44:57Code vs. text in technical books
- 47:25Reading and research while writing
- 50:36Future book topics
- 52:42Getting started with writing
- 55:51Parting advice and reflections
related talks
53mEverything You Need to Know About Writing Technical Python Books
32mKeynote: AI & the stuff built for AI - are they actually useful for data science?
53mData Engineering and AI
19mNow in AI - How We Got Here
05mOvercoming challenges when implementing AI
26mI’m Not Writing Code Anymore—Now What? | The 7-Step Workflow
45mDeveloper experience in the AI era
48mNavigating the Divide: Open vs. Proprietary AI