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[{"title": "Managing Your Account", "body": "<p>Having an online account with us allows you to check out faster, earn points that accrue toward rewards, review past orders, save frequently used addresses, and get the latest on sales and new arrivals. All you need to <a href=\"/my-account/sign-in\">create an account</a> is a valid email address.</p>\n<p>Learn more about <a href=\"https://www.gapinc.com/en-us/consumer-privacy-policy\" target=\"_blank\">our privacy policy</a>.</p>", "url": "https://www.gap.com/customerService/info.do?cid=81269", "doc_id": "e9b7ccddd5397baab55851aa31191022"}, {"title": "Why Am I Being Asked to Update My Password?", "body": "<div class=\"accordion-details\" id=\"whyamibeingaskedtoupdatemypasswordBody\">\n<p>Providing our customers with a safe and secure shopping experience is very important to us. We know that many customers re-use their passwords across multiple websites, and if any of those sites are compromised, it can put your online security at risk. That's why it's a good id

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sudo apt-get -y install software-properties-common
sudo add-apt-repository -y ppa:certbot/certbot
sudo apt-get -y update
sudo apt-get -y install certbot

sudo service openvpnas stop

sudo certbot certonly \
  --standalone \
  --non-interactive \

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version: "3"

networks:

  default:

  proxy:
    external: true

volumes:

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class Attention(Module):
    """
    Computes a weighted average of channels across timesteps (1 parameter pr. channel).
    """
    def __init__(self, attention_size, return_attention=False):
        """ Initialize the attention layer
        # Arguments:
            attention_size: Size of the attention vector.
            return_attention: If true, output will include the weight for each input token
                              used for the prediction

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# WARNING: This docker-compose.yml is only for testing purpose.
# Parameters:
# - name: CONFLUENT_PLATFORM_VERSION
#   default: 3.0.0
#   reference: https://hub.docker.com/u/confluentinc/
# Ports:
# - description: Major ports are exposed to host computer
# - zookeeper: 2181
#      kafka1: 9091
#      kafka2: 9092

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# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD+Patents license found in the
# LICENSE file in the root directory of this source tree.

# -*- makefile -*-
# tested on CentOS 7, Ubuntu 16 and Ubuntu 14, see below to adjust flags to distribution.

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import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size, n_layers=1):
        super(RNN, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

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Comprehensive Texts - Books

• Deep Learning - Goodfellow, Bengio, Courville
  • Section 1: ML basics (skipped)
  • Section 2: DL
  • Section 3: DL in practice & research
  • Summary - Notes by someone
• ⭐ Deep Learning in Neural Networks: An Overview - Schmidhuber
• [ML yearning - Andrew Ng] (http://www.mlyearning.org/): general Machine Learning skills (not fully available yet)

Courses and long reads

• A Statistical View of Deep Learning (many blog posts)

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class attentionalLSTMCell(nn.Module):
    def __init__(self, input_size, hidden_size, num_variants):
        super(attentionalLSTMCell, self).__init__()
        self.hidden_size = hidden_size
        self.input_size = input_size
        self.num_variants = num_variants
        
        self.ih = nn.Linear(self.input_size, 4 * self.hidden_size * self.num_variants)
        self.hh = nn.Linear(self.hidden_size, 4 * self.hidden_size * self.num_variants)