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Pablo N. Marino pablonm3

  • Quark.ai
  • Buenos Aires, Argentina
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@mbollmann
mbollmann / attention_lstm.py
Last active June 26, 2023 10:08
My attempt at creating an LSTM with attention in Keras
class AttentionLSTM(LSTM):
"""LSTM with attention mechanism
This is an LSTM incorporating an attention mechanism into its hidden states.
Currently, the context vector calculated from the attended vector is fed
into the model's internal states, closely following the model by Xu et al.
(2016, Sec. 3.1.2), using a soft attention model following
Bahdanau et al. (2014).
The layer expects two inputs instead of the usual one:

Just a Handy Nylas client based on curl

curl https://gist.githubusercontent.com/gregory/3c98a10cc009ae9f333cd0018523afc5/raw/40917888cd1e903bbd9ab0cbe33d6863728bcf4b/nylas.sh --output /usr/local/bin/nylas
chmod +x /usr/local/bin/nylas
/usr/local/bin/nylas
@zthxxx
zthxxx / Activate Office 2019 for macOS VoL.md
Last active July 24, 2024 23:47
crack activate Office on mac with license file
@chdsbd
chdsbd / slack-dnd.md
Created September 16, 2018 04:15
Disable Slack notifications for the weekend

Disable Slack notifications for the weekend

There isn't a way to configure DND for the weekend on Slack. However, you can use the /dnd 24 hours command to disable notifications for 24 hours using the [undocumented API][api] and [legacy tokens][token]. We're going to use the undocumented api and IFTTT to automate this.

Steps

  1. Generate a [token] for your account
  2. Navigate to your personal DM and copy the url (e.g. DAYM93S0N from acme.slack.com/messages/DAYM93S0N/convo/GBG661YR2-1536698414.000100/)
  3. Update the following url with your information (TOKEN looks like xoxp-4030334043-4959593942-3030320302032-sdf23rfasdf23rsf and CHANNEL looks like DAYM93S0N)
    https://slack.com/api/chat.command?token=xoxp-4030334043-4959593942-3030320302032-sdf23rfasdf23rsf&channel=DAYM93S0N&command=%2Fdnd&text=24%20hours
@pablonm3
pablonm3 / digit_classification.ipynb
Last active August 28, 2019 05:50
+98% accuracy on digit classification using DNNs with Keras lib and simple experiments to try to improve its accuracy
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