Webinar: Image Similarity: Deep Learning and Beyond (January 12th/Register for Recording)

Webinar: Image Similarity: Deep Learning and Beyond by Dato.

From the webpage:

In this talk, we will extract features from the convolutional networks applied to real estate images to build a similarity graph and then do label propagation on the images to label different images in our dataset.

Recommended for:

  • Data scientists and engineers
  • Developers and technical team managers
  • Technical product managers

What you’ll learn:

  • How to extract features from a convolutional network using GraphLab Create
  • How to build similarity graphs using nearest neighbors
  • How to implement graph algorithms such as PageRank using GraphLab Create

What we’ll cover:

  • Extracting features from convolutional networks
  • Building similarity graphs using nearest neighbors
  • Clustering: kmeans and beyond
  • Graph algorithms: PageRank and label propagation

I had mixed results with webinars in 2015.

Looking forward to this one because of the coverage of similarity graphs.

From a subject identity perspective, how much similarity do you need to be the “same” subject?

If I have two books, one printed within the copyright period and another copy printed after the work came into the public domain, are they the same subject?

For some purposes yes and for other purposes not.

The strings we give web browsers, usually starting with “https://” these days, are crude measures of subject identity, don’t you think?

I say “the strings we give web browsers” as the efforts of TBL and his cronies to use popularity as a measure of success, continue their efforts to conflate URI, IRI, and URL into only URL. https://url.spec.whatwg.org/ The simplification doesn’t bother me as much as the attempts to conceal it.

It’s one way to bolster a claim to have anyways been right, just re-write the records that anyone is likely to remember. I prefer my history with warts and all.

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