Grand Canyon
A stormy day at Grand Canyon National Park, just a few minutes later a hail / snow mix developed.
Related Posts
(Published to the Fediverse as: Grand Canyon #photo #grandcanyon A stormy and dramatic view of Grand Canyon National Park. )
Hoover Dam (Fixed)
The angle of an electricity transmission tower at the Hoover Dam was really bothering me until I fixed it in post.
Related Posts
- Doyle Drive: Before and After
- Bay Bridge, Old And New
- Bay Bridge Approach
- Sierra Nevada
- Train of Lights
(Published to the Fediverse as: Hoover Dam (Fixed) #photo #dam Hoover Dam, corrected so the electricity transmission tower looks right. )
High Roller Timelapse
Sunset timelapse of the 550 foot Ferris Wheel at The Linq in Las Vegas, Nevada (technically the High Roller Observation Wheel).
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- Sunset #11
- Milky Way Rises over South Lake Tahoe
- Timelapse, Week of Jan 31 2022
- Spherical Sunset
- Sunset sunset
(Published to the Fediverse as: High Roller Timelapse #timelapse #video A sunset timelapse of the Ferris Wheel at The Linq in Las Vegas, Nevada (the High Roller Observation Wheel). )
Autumnal Equinox 2018
It's Autumn for the Northern Hemisphere and Spring south of the equator.
Rendered in Catfood Earth.
(Previously, Previously, Previously, Previously, Previously)
Related Posts
- Autumnal Equinox 2016
- Vernal (Spring) Equinox 2018
- Autumnal Equinox 2019
- Autumnal Equinox 2013
- Autumnal Equinox 2017
(Published to the Fediverse as: Autumnal Equinox 2018 #code #earth #equinox #autumnal The moment of Autumnal Equinox 2018 (the start of Autumn in the Northern Hemisphere) rendered in Catfood Earth. )
Better related posts with word2vec (C#)
I have been experimenting with word2vec recently. Word2vec trains a neural network to guess which word is likely to appear given the context of the surrounding words. The result is a vector representation of each word in the trained vocabulary with some amazing properties (the canonical example is king - man + woman = queen). You can also find similar words by looking at cosine distance - words that are close in meaning have vectors that are close in orientation.
This sounds like it should work well for finding related posts. Spoiler alert: it does!
My old system listed posts with similar tags. This worked reasonably well, but it depended on me remembering to add enough tags to each post and a lot of the time it really just listed a few recent posts that were loosely related. The new system (live now) does a much better job which should be helpful to visitors and is likely to help with SEO as well.
I don't have a full implementation to share as it's reasonably tightly coupled to my custom CMS but here is a code snippet which should be enough to get this up and running anywhere:
The first step is getting a vector representation of a post. Word2vec just gives you a vector for a word (or short phrase depending on how the model is trained). A related technology, doc2vec, adds the document to the vector. This could be useful but isn't really what I needed here (i.e. I could solve my forgetfulness around adding tags by training a model to suggest them for me - might be a good project for another day). I ended up using a pre-trained model and then averaging together the vectors for each word. This paper (PDF) suggests that this isn't too crazy.
For the model I used word2vec-slim which condenses the Google News model down from 3 million words to 300k. This is because my blog runs on a very modest EC2 instance and a multi-gigabyte model might kill it. I load the model into Word2vec.Tools (available via NuGet) and then just get the word vectors (GetRepresentationFor(...).NumericVector) and average them together.
I haven't included code to build the word list but I just took every word from the post, title, meta description and tag list, removed stop words (the, and, etc) and converted to lower case.
Now that each post has a vector representation it's easy to compute the most related posts. For a given post compute the cosine distance between the post vector and every other post. Sort the list in ascending order and pick however many you want from the top (the distance between the post and itself would be 1, a totally unrelated post would be 0). The last line in the code sample shows this comparison for one post pair using Accord.Math, also on Nuget.
I'm really happy with the results. This was a fast implementation and a huge improvement over tag based related posts.
Updated 2023-01-29 22:00:
I have recently moved this functionality to use an OpenAI embeddings API integration.
Related Posts
- Upgrading from word2vec to OpenAI
- Geotagging posts in BlogEngine.NET
- BlogEngine.NET most popular pages widget using Google Analytics
- Migrating from Blogger to BlogEngine.NET
- Correlation is not causation but...
(Published to the Fediverse as: Better related posts with word2vec (C#) #code #software #word2vec #ithcwy #ml How to use word2vec to create a vector representation of a blog post and then use the cosine distance between posts to select improved related posts. )
Lassen Star Trails
A timelapse two ways shot from the Manzanita Lake campground at Lassen Volcanic National Park (the second time I've visited and the second time that Bumpass Hell has been closed). First a regular 4K timelapse looking up from the campsite:
The second version is the same footage in HD where each frame is the cumulative maximum pixel value of all the frames up to the current frame (so it builds in star trails as the video runs):
Related Posts
- Bumpass Hell
- Storm builds over Lassen Peak (Timelapse)
- Stars over Lake Tahoe
- Milky Way Rises over South Lake Tahoe
- Sugarloaf Stars and Milky Way
(Published to the Fediverse as: Lassen Star Trails #timelapse #video #4k #lassen #stars Cumulative star trails and a regular night timelapse shot from the Manzanita Lake campground at Lassen Volcanic National Park in California. )
Draw the rest of the Hummingbird
A genetic algorithm learns to draw a hummingbird:
The video is an animation of three thousand generations of evolution. It starts with a random mix of line segments which are then mutated by adding or removing lines and by changing the start, end and color of existing lines. Each generation has 32 individuals. The best individual is mutated to create the next generation.
For this implementation the best or fittest individual is the one with the least error on a pixel by pixel comparison to a stock art drawing of a hummingbird. Because I care more about the shape than completely filling in the drawing an error outside the figure is penalized three times more than a gap inside the figure.
Color is mutated each generation but not selected for, so it's just changing randomly.
Related Posts
(Published to the Fediverse as: Draw the rest of the Hummingbird #etc #video #genetic #hummingbird Animation of three thousand generations of a generic algorithm learning how to draw a hummingbird. )
Hoylake Beach
Timelapse looking over Hoylake beach in the Wirral, UK.
Related Posts
(Published to the Fediverse as: Hoylake Beach #timelapse #video #hoylake Timelapse of Hoylake Beach in the Wirral, United Kingdom. )