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on 23-Sep-2024 (Mon)

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Flashcard 7658337078540

Tags
#recurrent-neural-networks #rnn
Question

non-contractual settings

The specific challenge in such settings is to accurately and timely inform managers on the subtle distinction between a [...] event (i.e., a customer stops doing business with the focal firm) and an extended period of inactivity of their customers, because possible marketing implications are completely different in each of these situations.

Answer
pending defection

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non-contractual settings The specific challenge in such settings is to accurately and timely inform managers on the subtle distinction between a pending defection event (i.e., a customer stops doing business with the focal firm) and an extended period of inactivity of their customers, because possible marketing implications are completely differe

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Flashcard 7658339175692

Tags
#deep-learning #keras #lstm #python #sequence
Question
[...] a dataset involves rescaling the distribution of values so that the mean of observed values is 0 and the standard deviation is 1
Answer
Standardizing

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Standardizing a dataset involves rescaling the distribution of values so that the mean of observed values is 0 and the standard deviation is 1

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Flashcard 7658341010700

Tags
#tensorflow #tensorflow-certificate
Question
# [...] seed
tf.random.set_seed(42)

#operation seed
tf.random.shuffle(not_shuffled, seed=42)

Answer
global

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Tensorflow random
# global seed tf.random.set_seed(42) #operation seed tf.random.shuffle(not_shuffled, seed=42)






Flashcard 7658343107852

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question

The learning mechanism of the recurrent neural network thus involves:

(1) the [...] step where the cross- entropy loss is calculated;

Answer
forward propagation

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The learning mechanism of the recurrent neural network thus involves: (1) the forward propagation step where the cross- entropy loss is calculated;

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Flashcard 7658344942860

Tags
#deep-learning #keras #lstm #python #sequence
Question
Unfortunately, the range of [...] information that standard RNNs can access is in practice quite limited. The problem is that the influence of a given input on the hidden layer, and therefore on the network output, either decays or blows up exponentially as it cycles around the network’s recurrent connections.
Answer
contextual

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Unfortunately, the range of contextual information that standard RNNs can access is in practice quite limited. The problem is that the influence of a given input on the hidden layer, and therefore on the network output, eith

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Flashcard 7658346777868

Tags
#feature-engineering #lstm #recurrent-neural-networks #rnn
Question
The HMM has N discrete hidden states (where N is typically small) and, therefore, has only [...] bits of information available to capture the sequence history (Brown & Hinton, 2001)
Answer
log 2 (N)

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The HMM has N discrete hidden states (where N is typically small) and, therefore, has only log 2 (N) bits of information available to capture the sequence history (Brown & Hinton, 2001)

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