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Latent Dirichlet Allocation algorithms for topic extraction from the arXiv pre-print data

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KA Lite central server code (wraps the ka-lite repository and adds features)

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Basic django project (demo of model, view, and template)

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A tutorial that shows the powerful capabilities of the computer algebra system SymPy for solving problems of high school math, calculus, mechanics, vectors, and linear algebra problems.

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issue commentbfortuner/ml-glossary

Cross Product vs Element by Element Multiplication

Definitely no cross product invovled... cross product is a "hack" for getting perpendicular vectors and only applies for 3D vectors.

I'm guessing what is meant is just the "matrix product"

  • think of x1 is also a row vector
  • and (targets - predictions) as a column vector
  • then the Python matrix-multiply operator @ corresponds to row-times-column style matrix product which is what we want (equivalent to computing the dot product, .dot)

Intuitively, the d for each weight has a contribution from each data point (n=200) so dot product by it's summy nature is the convenient tool for doing this.

see

import numpy as np

u = np.array([1,3,3])
v = np.array([2,2,3])

print("The dot product between u and v can be computer as...")
print("The sum of the elementwise-wise products", sum(u.T*v))
print("The matrix product", u@v)
print("Or by calling the .dot product on one-a-dem vecs", u.dot(v), v.dot(u))

@pavelbrn If you have time to fix this, perhaps you can open a PR with change:

# Use matrix cross product (*) to simultaneously
# calculate the derivative for each weight
d_w1 = -x1*(targets - predictions)

to

# Use dot product to calculate the derivative for each weight
d_w1 = -x1.dot(targets - predictions)

(I removed the whole "simulatanous" part because it doesn't apply here. Simultanous way would be to compute d as 3D vector where matrix-vector product would be useful, but the code shows cleaner coefficient-by-coefficient approach so matrix product not involved

pavelbrn

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Grade levels controlled vocab

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added GCP subjects: Reading and Mathematics

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Add PDFs of latest 2020 documents GCF as PDF

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Global Proficiency Framework for Reading and Mathematics

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issue openedscipy/docs.scipy.org

Bad relatie links on https://docs.scipy.org/doc/scipy/reference/

Hello, I was recently browsing the scipy docs reference/ site: https://docs.scipy.org/doc/scipy/reference/index.html and noticed some link problems with the nav bar:

  • the link "Getting started" 404s: https://docs.scipy.org/doc/scipy/reference/getting_started.html
  • navigating to "API reference" renders weirdly https://docs.scipy.org/doc/scipy/reference/reference/index.html because the _image and _static assets are not present

The same two links work fine work on the main docs page: https://docs.scipy.org/doc/scipy/

  • the link "Getting started" points to https://docs.scipy.org/doc/scipygetting_started.html
  • the link "API reference" points to https://docs.scipy.org/doc/scipy/reference/index.html

so the seems to be the relative links in the header:

  • when resolved relative to https://docs.scipy.org/doc/scipy/ the links are fine
  • when resolved relative to https://docs.scipy.org/doc/scipy/reference/ the relative link don't work

One possible fix would be to use absolute paths in the nav header

  • /doc/scipy/reference/getting_started.html
  • /doc/scipy/reference/index.html

Screen Shot 2021-09-17 at 12 47 27 PM

Unfortunately I wasn't able to find the source code for this element to make this change. If someone were to point me in the right direction, I'll be happy to submit a PR to fix this.

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put back versionned links for scipy

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PR closed ivanistheone/docs.scipy.org

fix hardlinks to numpy v1.17.0 assets

currently:

numpy/numpy-ref-1.17.0.pdf
is
https://docs.scipy.org/doc/numpy/numpy-ref-1.17.0.pdf
-->
https://numpy.org/doc/stable/numpy-ref-1.17.0.pdf
which 404s

this is an attempt to fix with absolute URL https://numpy.org/doc/1.21/numpy-ref.pdf

perhaps even better would be https://numpy.org/doc/stable/numpy-ref.pdf

+5 -5

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pull request commentivanistheone/docs.scipy.org

fix hardlinks to numpy v1.17.0 assets

see https://github.com/scipy/docs.scipy.org/pull/56 instead

ivanistheone

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PR opened scipy/docs.scipy.org

Fix pdf links

currently:

numpy/numpy-ref-1.17.0.pdf
is
https://docs.scipy.org/doc/numpy/numpy-ref-1.17.0.pdf
-->
https://numpy.org/doc/stable/numpy-ref-1.17.0.pdf
which 404s

this is an attempt to fix this by removing version number, but still using the /doc/numpy/ --> https://numpy.org/doc/stable/ redirect

+5 -5

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fix

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commit sha e620271fc388debafb1f5029af2bda85acea1e7f

take advantage of /docs/numpy/ redirects much simpler... though the section **Others:** seems outdated (maybe this is auto-generated file?)

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PR opened ivanistheone/docs.scipy.org

fix hardlinks to numpy v1.17.0 assets

currently:

numpy/numpy-ref-1.17.0.pdf

https://docs.scipy.org/doc/numpy/numpy-ref-1.17.0.pdf
-->
https://numpy.org/doc/stable/numpy-ref-1.17.0.pdf
which 404s

this is an attempt to fix with absolute URL https://numpy.org/doc/1.21/numpy-ref.pdf

perhaps even better would be https://numpy.org/doc/stable/numpy-ref.pdf

+1 -1

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Reusable automation tasks and scripts based on fab-classic: cloud provisiotning, docker, etc.

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issue openedlearningequality/kolibri

Three small styling bugs in exercise renderer

1/

Font and styling text inside <pre> tag changes drastically between responsive breakpoints Screenshot_2020-03-17 Preguntas de Evaluación - Option 2 - CommonLit Test - Kolibri


2/

Horizontal line separators between answers stick out too far on smallest responsive brekpoint Screenshot_2020-03-17 Preguntas de Evaluación - Option 2 - CommonLit Test - Kolibri(2)

for 1/ and 2/ see http://35.185.120.102/en/learn/#/topics/c/ba2b32b657104fe4b73748191693a725


3/

Hint numbering appears weird Screen Shot 2020-03-20 at 4 17 16 PM see https://kolibridemo.learningequality.org/en/learn/#/topics/c/ba92f5cc02e7528abaf36108262f4772

Of these only 1/ is important because we'll be using it for CommonLit text passages and will get a lot of external visibility in coming weeks.

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