# on 23-Apr-2024 (Tue)

#### Flashcard 7625143094540

Tags
#deep-learning #keras #lstm #python #sequence
Question
Careful choice must be given to the number of [...] specified when preparing your input data for sequence prediction problems in Keras
time steps

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

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Careful choice must be given to the number of time steps specified when preparing your input data for sequence prediction problems in Keras

#### Original toplevel document (pdf)

cannot see any pdfs

#### Flashcard 7625145715980

Tags
#recurrent-neural-networks #rnn
Question
The challenge to derive such individual-level predictions is particularly demanding in the context of non-contractual settings (such as most [...] businesses, online media consumption, charity donations).
retail

status measured difficulty not learned 37% [default] 0

#### Parent (intermediate) annotation

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The challenge to derive such individual-level predictions is particularly demanding in the context of non-contractual settings (such as most retail businesses, online media consumption, charity donations).

#### Original toplevel document (pdf)

cannot see any pdfs

#### Flashcard 7625148599564

Tags
#tensorflow #tensorflow-certificate
Question
print('Datatype of every element:', A.dtype)
print('Number of dimensions (rank):', A.ndim)
print('Shape of tensor:', A.shape)
print('Elements along the 0 axis:', A.shape[0])
print('Elements along the last axis:',  [...])
print('Total number of elements:', tf.size(A))
print('Total number of elements:', tf.size(A).numpy())

Output:
Datatype of every element: <dtype: 'int64'>
Number of dimensions (rank): 4
Shape of tensor: (2, 3, 4, 5)
Elements along the 0 axis: 2
Elements along the last axis: 5
Total number of elements: tf.Tensor(120, shape=(), dtype=int32)
Total number of elements: 120

A.shape[-1]

status measured difficulty not learned 37% [default] 0

Properties of tensor
every element:', A.dtype) print('Number of dimensions (rank):', A.ndim) print('Shape of tensor:', A.shape) print('Elements along the 0 axis:', A.shape[0]) print('Elements along the last axis:', <span>A.shape[-1]) print('Total number of elements:', tf.size(A)) print('Total number of elements:', tf.size(A).numpy()) Output: Datatype of every element: <dtype: 'int64'> Number of dimensions (ra

#### Flashcard 7625150434572

Tags
#algebra #matrix #tensorflow #tensorflow-certificate
Question

In General:

To multiply an m×n matrix by an n×p matrix, the ns must be [...],
and the result is an m×p matrix.

the same

status measured difficulty not learned 37% [default] 0

Matrix multiplication
In General: To multiply an m×n matrix by an n×p matrix, the ns must be the same, and the result is an m×p matrix.

#### Flashcard 7625152793868

Tags
#conv2D #convolution #tensorflow #tensorflow-certificate
Question
Step 1 is to gather the data. You'll notice that there's a bit of a change here in that the training data needed to be reshaped. That's because the first convolution expects a [...] containing everything, so instead of 60,000 28x28x1 items in a list, we have a single 4D list that is 60,000x28x28x1,