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Flashcard 7624071187724

Tags

#tensorflow #tensorflow-certificate

Question

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

model = Sequential([...](1, input_shape=[1]))
model.compile(optimizer='sgd', loss='mean_squared_error')
xs = np.array([1,5,12,-1,10], dtype=float)
ys = np.array([5,13,27,1,23], dtype=float)
model.fit(xs, ys, epochs=500)
model.predict(x=[15])

Answer

Dense

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scheduled repetition interval		last repetition or drill

Tensorflow basics - typical flow of model building
from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense import numpy as np model = Sequential(Dense(1, input_shape=[1])) model.compile(optimizer='sgd', loss='mean_squared_error') xs = np.array([1,5,12,-1,10], dtype=float) ys = np.array([5,13,27,1,23], dtype=float) model.fit(xs, ys, ep

Flashcard 7624087178508

Tags

#tensorflow #tensorflow-certificate

Question

import tensorflow as tf

#stop training after reaching accuract of 0.99
class MyCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if [...].get('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      self.model.stop_training = True

Answer

logs

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Tensorflow - callbacks
import tensorflow as tf #stop training after reaching accuract of 0.99 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') self.model.stop_training = True

Flashcard 7624233192716

Tags

#tensorflow #tensorflow-certificate

Question

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


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


another_matrix.ndim
2

Answer

tf.float16

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Tensors fundamentals - creating tensors
# Create matrix another_matrix = tf.constant([[10. ,66.], [5. , 9.], [13. , 4.]], dtype=tf.float16) another_matrix <tf.Tensor: shape=(3, 2), dtype=float16, numpy= array([[10., 66.], [ 5., 9.], [13., 4.]], dtype=float16)> another_matrix.ndim 2

Flashcard 7624249969932

Tags

#tensorflow #tensorflow-certificate

Question

changeable_tensor = tf.Variable([10, 7])

changeable_tensor[0] = 77

Output:
TypeError: 'ResourceVariable' object does not support item assignment


changeable_tensor[0].[...]

Output:
<tf.Variable 'UnreadVariable' shape=(2,) dtype=int32, numpy=array([77,  7], dtype=int32)>

Answer

assign(77)

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Tensorflow basics
changeable_tensor = tf.Variable([10, 7]) changeable_tensor[0] = 77 Output: TypeError: 'ResourceVariable' object does not support item assignment changeable_tensor[0].assign(77) Output: <tf.Variable 'UnreadVariable' shape=(2,) dtype=int32, numpy=array([77, 7], dtype=int32)>

Flashcard 7625344683276

Tags

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

Question

The LSTM neural network typology is well-suited for modeling churn, especially in [...] format. However, its performance against standard churn prediction models remains an avenue for further research

Answer

time-series

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Parent (intermediate) annotation

Open it
The LSTM neural network typology is well-suited for modeling churn, especially in time-series format. However, its performance against standard churn prediction models remains an avenue for further research

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Annotation 7625357790476

#ML-engineering #ML_in_Action #learning #machine #software-engineering

In the experimentation phase, the largest causes of project failure are either the experimentation taking too long (testing too many things or spending too long fine- tuning an approach)

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Open it
In the experimentation phase, the largest causes of project failure are either the experimentation taking too long (testing too many things or spending too long fine- tuning an approach) or an underdeveloped prototype that is so abysmally bad that the business decides to move on to something else.

Original toplevel document (pdf)

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Annotation 7625359363340

#ML-engineering #ML_in_Action #learning #machine #software-engineering

In the experimentation phase, the largest causes of project failure are either...... or an underdeveloped prototype that is so abysmally bad that the business decides to move on to something else.

status	not read	reprioritisations
last reprioritisation on		suggested re-reading day
started reading on		finished reading on

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Original toplevel document (pdf)

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Flashcard 7625361722636

Tags

#recurrent-neural-networks #rnn

Question

it also accurately predicts periods of elevated transaction activity and captures other forms of purchase dynamics that can be leveraged in [...] of future sequences of customer transactions.

Answer

simulations

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scheduled repetition interval		last repetition or drill

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Open it
it also accurately predicts periods of elevated transaction activity and captures other forms of purchase dynamics that can be leveraged in simulations of future sequences of customer transactions.

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Flashcard 7625364344076

Tags

#tensorflow #tensorflow-certificate

Question

# Tensors can be indexed just like Python lists.

# Get the [...] 2 elements of each dimension
A[:2, :2, :2, :2]

Answer

first

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Tensors indexing
# Tensors can be indexed just like Python lists. # Get the first 2 elements of each dimension A[:2, :2, :2, :2]

Flashcard 7625366179084

Tags

#tensorflow #tensorflow-certificate

Question

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


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


another_matrix.ndim
[...]

Answer

status	not learned	measured difficulty	37% [default]	last interval [days]
repetition number in this series	0	memorised on		scheduled repetition
scheduled repetition interval		last repetition or drill

Tensors fundamentals - creating tensors
6.], [5. , 9.], [13. , 4.]], dtype=tf.float16) another_matrix <tf.Tensor: shape=(3, 2), dtype=float16, numpy= array([[10., 66.], [ 5., 9.], [13., 4.]], dtype=float16)> another_matrix.ndim <span>2 <span>

Flashcard 7625368538380

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 [...]. That's because the first convolution expects a single tensor containing everything, so instead of 60,000 28x28x1 items in a list, we have a single 4D list that is 60,000x28x28x1,

Answer

reshaped

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Open it
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 single tensor containing everything, so instead of 60,000 28x28x1 items in a list, we have a single 4D list that is 60,000x28x28x1, </spa

Original toplevel document

Convolution Neural Network - introduction
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 single tensor containing everything, so instead of 60,000 28x28x1 items in a list, we have a single 4D list that is 60,000x28x28x1, and the same for the test images. If you don't do this, you'll get an error when training as the Convolutions do not recognize the shape. import tensorflow as tf mnist = tf.keras.datase

Edited, memorised or added to reading queue

on 26-Apr-2024 (Fri)

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Original toplevel document (pdf)

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Original toplevel document (pdf)

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Original toplevel document (pdf)

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Original toplevel document (pdf)

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Original toplevel document