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on 29-Oct-2025 (Wed)

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Annotation 7627490331916

[unknown IMAGE 7626420784396] #has-images #tensorflow #tensorflow-certificate

X_train, X_test = tf.split(tf.random.shuffle(X, seed=42), num_or_size_splits=[40, 10]) 

def plot_predictions(train_data = X_train, train_labels = y_train, test_data = X_test, test_labels = y_test, predictions = y_pred): 
""" Plots training data, testing_data """ 
  plt.figure(figsize=(10, 7))
  plt.scatter(train_data, train_labels, c="blue", label='Training data') 
  plt.scatter(test_data, test_labels, c="green", label="Testing data") 
  plt.scatter(test_data, predictions, c="red", label="Predictions") 
  plt.legend();

Common regression evaluation metrics
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TfC 01 regression
iment 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) <span>X_train, X_test = tf.split(tf.random.shuffle(X, seed=42), num_or_size_splits=[40, 10]) def plot_predictions(train_data = X_train, train_labels = y_train, test_data = X_test, test_labels = y_test, predictions = y_pred): """ Plots training data, testing_data """ plt.figure(figsize=(10, 7)) plt.scatter(train_data, train_labels, c="blue", label='Training data') plt.scatter(test_data, test_labels, c="green", label="Testing data") plt.scatter(test_data, predictions, c="red", label="Predictions") plt.legend(); Common regression evaluation metrics keyboard_arrow_down Introduction For regression problems: MAE tf.keras.losses.MAE() tf.metrics.mean_absolute_error() great starter metrics for any regression problem MSE tf.keras.losses



Flashcard 7760709291276

Tags
#DAG #causal #edx
Question
the most important take-home message: we need [...] to determine if we should adjust for a variable. The statistical criteria are insufficient to characterize confounding and confounders.
Answer
expert knowledge

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the most important take-home message: we need expert knowledge to determine if we should adjust for a variable. The statistical criteria are insufficient to characterize confounding and confounders.

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Flashcard 7761664281868

Tags
#recurrent-neural-networks #rnn
Question
non-contractual business settings (i.e., when the time at which a customer becomes inactive is [...] by the firm)
Answer
unobserved

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non-contractual business settings (i.e., when the time at which a customer becomes inactive is unobserved by the firm)

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Flashcard 7763364809996

Tags
#deep-learning #has-images #keras #lstm #python #sequence
[unknown IMAGE 7104054824204]
Question
You can specify the [...] argument that expects a tuple containing the number of time steps and the number of features
Answer
input shape

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You can specify the input shape argument that expects a tuple containing the number of time steps and the number of features

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Flashcard 7766198324492

Tags
#ML_in_Action #learning #machine #software-engineering
Question
ML engineering applies a system around this staggering level of complexity. It uses a set of standards, tools, [...], and methodology that aims to minimize the chances of abandoned, misguided, or irrelevant work being done in an effort to solve a business problem or need.
Answer
processes

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ML engineering applies a system around this staggering level of complexity. It uses a set of standards, tools, processes, and methodology that aims to minimize the chances of abandoned, misguided, or irrelevant work being done in an effort to solve a business problem or need.

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Annotation 7766199635212

[unknown IMAGE 7096178707724] #DAG #causal #edx #has-images #inference
As you may have already noticed, the case-control design selects individuals based on their outcome
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As you may have already noticed, the case-control design selects individuals based on their outcome. Women who did develop cancer are much more likely to be included in the study than women who did not develop cancer. Therefore, our causal graph will include a note for selection-- C--

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