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Flashcard 7734849309964

Tags

#deep-learning #embeddings

Question

With the similar idea of how we get word embeddings, we can make an analogy like this: a word is like a product; a sentence is like a sequence of ONE customer’s shopping sequence; [...] is like a sequence of ALL customers’ shopping sequence

Answer

an article

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repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

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With the similar idea of how we get word embeddings, we can make an analogy like this: a word is like a product; a sentence is like a sequence of ONE customer’s shopping sequence; an article is like a sequence of ALL customers’ shopping sequence

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Flashcard 7750775606540

Tags

#tensorflow #tensorflow-certificate

Question


another_matrix = tf.constant([[10. ,66.],
                              [5. , 9.],
                              [13. , 4.]], dtype=tf.float16)
another_matrix

Out:
<tf.Tensor: shape=(3, 2), dtype=float16, [...]=
array([[10., 66.],
       [ 5.,  9.],
       [13.,  4.]], dtype=float16)>

Answer

numpy

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repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

Tensorflow fundamentals
another_matrix = tf.constant([[10. ,66.], [5. , 9.], [13. , 4.]], dtype=tf.float16) another_matrix Out: <tf.Tensor: shape=(3, 2), dtype=float16, numpy= array([[10., 66.], [ 5., 9.], [13., 4.]], dtype=float16)>

Flashcard 7758738492684

Tags

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

Question

the RNN has distributed hidden states, which means that each input generally results in changes across [...] of the RNN (Ming et al., 2017).

Answer

all the hidden units

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the RNN has distributed hidden states, which means that each input generally results in changes across all the hidden units of the RNN (Ming et al., 2017).

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Annotation 7765905247500

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

RNNs combine a large number of distributed hidden states with nonlinear dynamics to update these hidden states, thereby allowing it to have a more substantial representational capacity when compared with an HMM

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mp; Hinton, 2001). On the other hand, the RNN has distributed hidden states, which means that each input generally results in changes across all the hidden units of the RNN (Ming et al., 2017). RNNs combine a large number of distributed hidden states with nonlinear dynamics to update these hidden states, thereby allowing it to have a more substantial representational capacity when compared with an HMM

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Annotation 7765907344652

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

All the analyst has to do is to encode each observation period (e.g., a day, a week, a month) as a vector of size 20, where all the values are equal to 0, except when a solicitation is sent, or a purchase is observed. If purchase seasonality is significant, e.g., if peaks in sales occur around Christmas, the analyst can also encode the current month using a one-hot vector of size 12, for a total vector length of 32 raw inputs.

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resented by elements in a vector. For instance, suppose a firm solicits customers either through phone, mail, or email (three channels), and customers may purchase across 17 product categories. All the analyst has to do is to encode each observation period (e.g., a day, a week, a month) as a vector of size 20, where all the values are equal to 0, except when a solicitation is sent, or a purchase is observed. If purchase seasonality is significant, e.g., if peaks in sales occur around Christmas, the analyst can also encode the current month using a one-hot vector of size 12, for a total vector length of 32 raw inputs.

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Flashcard 7765909179660

Tags

#has-images #tensorflow #tensorflow-certificate

[unknown IMAGE 7626420784396]

Question

How we can improve model (in the particular stage of the process)?

# 2. Compiling: change optimizer or its [...] (eg. learning rate)

Answer

parameters

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scheduled repetition interval		last repetition or drill

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How we can improve model (in the particular stage of the process)? # 2. Compiling: change optimizer or its parameters (eg. learning rate)

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TfC 01 regression
#### How we can improve model # 1. Creating model: add more layers, increase numbers of hidden neurons, change activation functions # 2. Compiling: change optimizer or its parameters (eg. learning rate) # 3. Fitting: more epochs, more data ### How? # from smaller model to larger model Evaluating models Typical workflow: build a model -> fit it -> evaulate -> tweak -> fit > evaluate -> .... Building model: experiment Evaluation model: visualize What

Flashcard 7765911276812

Tags

#has-images #tensorflow #tensorflow-certificate

[unknown IMAGE 7626420784396]

Question

What can visualize?

the data
model itself
the [...] of a model
predictions

Answer

training

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What can visualize? the data model itself the training of a model predictions

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TfC 01 regression
larger model Evaluating models Typical workflow: build a model -> fit it -> evaulate -> tweak -> fit > evaluate -> .... Building model: experiment Evaluation model: visualize What can visualize? the data model itself the training of a model predictions ## The 3 sets (or actually 2 sets: training and test set) tf.random.set_seed(999) X_train, X_test = tf.split(tf.random.shuffle(X, seed=42), num_or_size_splits=[40, 10]) def plot_predict

Flashcard 7765913111820

Tags

#deep-learning #keras #lstm #python #sequence

Question

There are some practical considerations when scaling sequence data.

Estimate Coefficients
You can estimate coefficients (min and max values for normalization or mean and standard deviation for standardization) from the training data. Inspect these first-cut estimates and use domain knowledge or domain experts to help improve these estimates so that they will be usefully correct on all data in [...].

Answer

the future

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zation) from the training data. Inspect these first-cut estimates and use domain knowledge or domain experts to help improve these estimates so that they will be usefully correct on all data in the future.

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Edited, memorised or added to reading queue

on 27-Oct-2025 (Mon)

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How we can improve model (in the particular stage of the process)?

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