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Adventures in Data Land
Alex Smola publishes Adventures in Data Land, a blog with posts on data and related subjects.
About Adventures in Data Land
Adventures in Data Land is a blog by Alex Smola, a machine learning researcher, with posts on data, statistics, machine learning and related subjects. The index page lists 48 post categories, each with a count, and shows dated posts with short excerpts. Recent posts cover efficient LLM inference, LLM evaluation and benchmarks, random projections, algorithmic fairness, and the MXNet deep learning framework.
The blog is a collection of written posts rather than a software tool, and the page does not describe any product functions. Pricing is unknown. The index shows only the most recent posts and categories, so it does not cover the full archive.
Key features
- Posts on hashing, random projections and sampling methods
- Posts on LLM evaluation, benchmarks and inference efficiency
- Posts on porting MXNet to Blackwell and Apple Silicon
- Category list with post counts, such as hashing (6) and evaluation (5)
- Announcement of Dive into Deep Learning, a book project in beta
Best for: Readers with a technical background in machine learning who want written discussion of hashing, random projections, LLM evaluation and related topics.
Good for
- Posts are grouped by topic tags, so readers can browse by subject.
- Recent posts include technical write-ups and tutorial material on LLM inference.
Watch out for
- The page is a blog index and does not describe software, tools or downloads.
- The index shows only some posts, so the full archive is not visible from this page.
Adventures in Data Land at a glance
- Website
- alex.smola.org/blog.html
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Adventures in Data Land FAQ
What topics does the blog cover?
Posts cover topics such as hashing, random projections, LLM evaluation and inference, algorithmic fairness, statistics, voice agents and the MXNet framework.
Is there a book associated with the blog?
A post announces Dive into Deep Learning, described as a book project still in beta stage at the time of that post.
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Tell us and an editor will check it.
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