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View .i3config
# This file has been auto-generated by i3-config-wizard(1).
# It will not be overwritten, so edit it as you like.
#
# Should you change your keyboard layout somewhen, delete
# this file and re-run i3-config-wizard(1).
#
# i3 config file (v4)
#
# Please see http://i3wm.org/docs/userguide.html for a complete reference!
View gist:8f6c49d709e9663544ef
/server add twitch irc.twitch.tv/6667 -password=oauth:****************************** -username=TWITCHUSERNAME -nicks=TWITCHUSERNAME
/join TWITCHCHANNEL
View lightfm_huge_embeds.ipynb
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@JasonTam
JasonTam / benchmark_sparse_pos_check.ipynb
Last active Jun 7, 2017
sparse neg sampling benchmark
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View avg_embs.ipynb
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@JasonTam
JasonTam / parq_vs_avro_msg.ipynb
Last active Oct 24, 2017
Comparison of avro, msgpack, parquet
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@JasonTam
JasonTam / approx_auc_dist.ipynb
Created Nov 1, 2017
Calculating AUC can take a long-ass time for large number of samples
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@JasonTam
JasonTam / lightfm_freeze-emb_example.ipynb
Last active Nov 7, 2017
example of how to freeze embeddings in lightfm by exploiting the accumulated gradient in adagrad
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@JasonTam
JasonTam / binary_search_membership.py
Last active Feb 28, 2018
Most of the time `in` is good enough (even for lists)
View binary_search_membership.py
from bisect import bisect_left
def in_bs(a, x):
i = bisect_left(a, x)
return (i != len(a)) and (a[i] == x)
@JasonTam
JasonTam / neg_samp.py
Last active Feb 28, 2018
vectorized negative sampling for sparse positive interactions
View neg_samp.py
"""
Pretty efficient way to sample negatives using binary search.
Imagine `pos_inds` are items a user has interacted with,
and we want a vectorized approach to uniformly sample many negative items.
This is better than (in the case where `n_samp` is large)
sampling from the entire catalog and then checking if it's a positive item.
Ahem... negative sampling for sparse interaction matrices.
"""
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