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on 16-Jan-2025 (Thu)

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Flashcard 7675826801932

Tags
#tensorflow #tensorflow-certificate
Question

# Create training and test datasets
#my way:

from sklearn.[...] import train_test_split 

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)

Answer
model_selection

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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# Create training and test datasets #my way: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)

Original toplevel document

TfC_01_FINAL_EXAMPLE.ipynb
nsurance_one_hot # Create X and y values (features and labels) y = insurance_one_hot['charges'] X = insurance_one_hot.drop('charges', axis=1) #y = y.values # This is not necessary #X = X.values <span>#X, y, X.shape, y.shape # Create training and test datasets #my way: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42) Preprocessing data (normalization and standardization) Preprocessing steps: Turn all data into numbers Make sure your tensors are in the right shape Scale features (normalize or standardize) Neural networks tend to pre






Flashcard 7675828636940

Tags
#tensorflow #tensorflow-certificate
Question

Multiclass image classificaton: pizza, steak, sushi

Input_shape = [None, 224, 224, 3] - single image

Input shape = [[...]] - common batch size of images

32 is a common batch size

Answer
32, 224, 224, 3

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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Multiclass image classificaton: pizza, steak, sushi Input_shape = [None, 224, 224, 3] - single image Input shape = [32, 224, 224, 3] - common batch size of images 32 is a common batch size

Original toplevel document

TfC_02_classification-PART_1
ssification - a sample can be assigned to more than one label from more than 2 label options Multiclass classification - a sample can be assigned to one label but from more than 2 label options <span>Multiclass image classificaton: pizza, steak, sushi Input_shape = [None, 224, 224, 3] - single image Input shape = [32, 224, 224, 3] - common batch size of images 32 is a common batch size How to generate such data? from sklearn.datasets import make_circles # Make 1000 examples n_samples=1000 # Create circles X, y = make_circles(n_samples, noise=0.03, random_state=42) How