Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
![Screenshot 2023-12-18 at 10 40 27 PM](https://private-user-images.githubusercontent.com/3837836/291468646-4c30ad72-76ee-4939-a5fb-16b570d38cf2.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.bpw7iqfoiw0umFdeoPl8PFQmLxeMVQh502My_-KWspQ)
from scipy.stats import dirichlet, poisson | |
from numpy.random import choice | |
num_documents = 5 | |
num_topics = 2 | |
topic_dirichlet_parameter = 1 # beta | |
term_dirichlet_parameter = 1 # alpha | |
vocabulary = ["see", "spot", "run"] | |
num_terms = len(vocabulary) |
docker rmi $(docker images -q -f dangling=true) |
<div id="chart"> | |
<h4>Percent of adults over 25 with at least a bachelor's degree:</h4> | |
<p><strong>Median:</strong> <span class="median"></span></p> | |
<small>Source: <cite><a href="http://census.gov">U.S. Census Bureau</a></cite>, via <cite><a href="http://beta.censusreporter.org/compare/01000US/040/table/?release=acs2011_1yr&table=B15003">Census Reporter</a></cite></small> | |
</div> |
# Editor backup files | |
*.bak | |
*~ |