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@skoqaq
skoqaq / build4123.sublime4.key
Last active June 6, 2024 06:54
Sublime Text 4 License Key
—– BEGIN LICENSE —–
Mifeng User
Single User License
EA7E-1184812
C0DAA9CD 6BE825B5 FF935692 1750523A
EDF59D3F A3BD6C96 F8D33866 3F1CCCEA
1C25BE4D 25B1C4CC 5110C20E 5246CC42
D232C83B C99CCC42 0E32890C B6CBF018
B1D4C178 2F9DDB16 ABAA74E5 95304BEF
9D0CCFA9 8AF8F8E2 1E0A955E 4771A576
@zhicongchen
zhicongchen / gensim_word2vec_procrustes_align.py
Last active June 3, 2024 01:54 — forked from quadrismegistus/gensim_word2vec_procrustes_align.py
Code for aligning two gensim word2vec models using Procrustes matrix alignment (updated for compatibility with Gensim 4.0 API). The code is modified from https://gist.github.com/quadrismegistus/09a93e219a6ffc4f216fb85235535faf, which is originally ported from HistWords by William Hamilton: https://github.com/williamleif/histwords
def smart_procrustes_align_gensim(base_embed, other_embed, words=None):
"""
Original script: https://gist.github.com/quadrismegistus/09a93e219a6ffc4f216fb85235535faf
Procrustes align two gensim word2vec models (to allow for comparison between same word across models).
Code ported from HistWords <https://github.com/williamleif/histwords> by William Hamilton <wleif@stanford.edu>.
First, intersect the vocabularies (see `intersection_align_gensim` documentation).
Then do the alignment on the other_embed model.
Replace the other_embed model's syn0 and syn0norm numpy matrices with the aligned version.
Return other_embed.
@cbaziotis
cbaziotis / AttentionWithContext.py
Last active April 25, 2022 14:37
Keras Layer that implements an Attention mechanism, with a context/query vector, for temporal data. Supports Masking. Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf] "Hierarchical Attention Networks for Document Classification"
def dot_product(x, kernel):
"""
Wrapper for dot product operation, in order to be compatible with both
Theano and Tensorflow
Args:
x (): input
kernel (): weights
Returns:
"""
if K.backend() == 'tensorflow':
@cbaziotis
cbaziotis / Attention.py
Last active March 28, 2023 11:50
Keras Layer that implements an Attention mechanism for temporal data. Supports Masking. Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]
from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer
def dot_product(x, kernel):
"""
Wrapper for dot product operation, in order to be compatible with both
Theano and Tensorflow
Args:
@Teino1978-Corp
Teino1978-Corp / MS Perks & Benefits.md
Created November 7, 2015 03:57
Summary of any and all perks when working with Microsoft.

##Perks of Microsoft

####Salary

####Health and Wellness Care

  • Medical and hospitalization: Two choices for medical insurance. Both with no premium but different deductibles.
    • The two choices on medical plans are a high-deductible health plan (Microsoft puts a chunk of money into the Health Savings Account for you, which covers most of the deductible) and an HMO.
  • With the HMO, you pay basically nothing as long as you only go to Group Health doctors. With the high-deductible plan, you're covered under the local Blue Cross provider which means you can go to just about any doctor in the country. For