Data Loaders in TensorFlow QUIZ (MCQ QUESTIONS AND ANSWERS)

To shuffle the dataset

To iterate over elements and their indices

To split the dataset

To apply a transformation to the dataset

By applying feature scaling

By using larger batch sizes

By using stratified sampling

By increasing the number of epochs

pad()

sequence_pad()

pad_sequences()

pad_data()

To shuffle the dataset

To interleave elements from multiple datasets

To apply a transformation to the dataset

To split the dataset

By removing samples with missing values

By replacing missing values with the mean

By increasing the batch size

By using smaller epochs

It caches the entire dataset in memory

It removes cached elements from memory

It shuffles the dataset

It applies a transformation to the dataset

split()

partition()

divide()

separate()

By ignoring minority classes

By duplicating samples from minority classes

By using stratified sampling

By increasing the learning rate

To shuffle the dataset

To group elements into batches

To apply a transformation to the dataset

To concatenate datasets

filter()

select()

extract()

include()

To slice the dataset into batches

To convert a tensor into a dataset

To shuffle the dataset

To apply a transformation to the dataset

By setting the batch size to 1

By using multi-threading or multiprocessing

By reducing the number of epochs

By increasing the learning rate

To remove elements from the dataset

To take a specified number of elements from the dataset

To shuffle the dataset

To apply a transformation to the dataset

Tokenization

Normalization

Downsampling

Vectorization

concat()

merge()

combine()

join()

tf.data

tf.train

tf.model

tf.load

By using NumPy arrays

By directly loading images into memory

By using tf.keras.preprocessing

By using tf.data.Dataset

It only supports small datasets

It provides efficient parallelism

It requires extensive manual coding

It is only compatible with TensorFlow 1.x

Preprocessing the data

Converting data to TensorFlow format

Installing necessary dependencies

Choosing a machine learning algorithm

iterate()

next_batch()

get_batch()

batch()

To load and preprocess data efficiently

To train machine learning models

To visualize data

To evaluate model performance

By adjusting parameters during model training

By setting batch size directly within the Data Loader

By using external scripts

By modifying the loss function

Normalization

Augmentation

Regularization

Dimensionality reduction

By using tf.data API methods

By modifying the TensorFlow core code

By using external libraries

By using pandas dataframes

map()

apply()

transform()

modify()

To preprocess data

To improve data loading performance

To visualize data

To shuffle data

repeat_count

iterations

repeat

epochs

By using external storage solutions

By downsampling the dataset

By using smaller batch sizes

By increasing the RAM capacity

Raises an error

Truncates the remaining samples

Automatically adjusts the batch size

Skips the last batch

shuffle()

randomize()

mix()

reorder()