Discover more from hrbrmstr's Daily Drop
*URLs; * For Beginners; ZomboDB
It'd take an effort of Herculean scale to pry my RSS feeds away from me. They're fixed more firmly in place than Mjölnir is to the ground when not in Thor's hands. I'm always on the lookout for new feeds or meta feeds, which I describe as a feed, produced by a third-party, that aggregates a bunch of other RSS-based content.
The fine folks over at Browserling publish many free resources for the broader community, and a recent(?) new resource is a set of these meta feeds by topic:
At present, each site is just some scrollable
divs with rendered RSS feeds for each publisher (here's the full list of over one hundred resources). There is also an option to search the feeds.
Browserling has plans to let you:
filter out links that contain topics you don't like
pin articles that are interesting to you to the top and receive optional push notifications as soon as these posts get published
receive a personalized RSS subscription that shows news from the sites you choose
I found some new feeds to add to my RSS collection, and I suspect you will as well.
* For Beginners
If you're up for more free (in this case, MIT licensed) AI/ML educational resources, look no further than (of all places) Microsoft, who has two "beginner" courses in artificial intelligence [GH] and machine learning [GH].
These aren't just high-level shills to get you to lay down some coin for Microsoft's Azure-specific services. Fundamental AI/ML literacy is almost a necessity in today's modern workplace, and especially so in the tech industry. Therefore, it is in the best interest of outfits such as Microsoft or Amazon to do what they can to ensure a solid AI/ML-educated hiring pipeline exists.
Microsoft's AI curriculum is based on PyTorch and TensorFlow and covers:
Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI).
Neural Networks and Deep Learning, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
Neural Architectures for working with images and text. We will cover recent models, but may lack a little bit on the state-of-the-art.
Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.
The lessons in their ML curriculum contain:
optional supplemental video
pre-lecture warmup quiz
for project-based lessons, step-by-step guides on how to build the project
and have both R and Python flavors to choose from.
Standalone items have intrinsic value all on their own, but when complementary items are paired together, they can become league more than just the sum of their parts. An example from your supermarket aisle might be that decadently sugary crème filling and circular chocolate cookie wafers are palate magnets on their own; when combined to form an Oreo, they become indistinguishable from crack.
ZDB's tagline is "Making Postgres and Elasticsearch work together like it's 2022", and is technically described as "a [100% native] Postgres extension that enables efficient full-text searching via the use of indexes backed by Elasticsearch."
ZDB implements Postgres' Index Access Method API, and you can use it in a straightforward fashion with a straightforward
CREATE INDEX ... USING zombodb on any existing Postgres table. ZDB fully manages a remote Elasticsearch index and guarantees transactionally-correct text-search query results.
Core features include:
MVCC-correct text-search and aggregation results
Managed and queried via standard SQL
Works with current Elasticsearch releases (no plugins required)
Scoring and Highlighting Support
Support for all Elasticsearch aggregations
Automatic Elasticsearch Mapping Generation
Ability to map custom domains
Per-field custom mappings
json/jsonbautomatically mapped as dynamic nested objects
Supports full set of
Elasticsearch language analyzers
Supports Elasticsearch's Similarity Module
Support for indexing & searching PostGIS
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