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TensorFlow Questions et reponses d'entretien

Differences associees

TensorFlow vs PyTorch

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Question 1. What is TensorFlow and explain its primary use?

TensorFlow is an open-source machine learning library developed by the Google Brain team. It is primarily used for building and training deep learning models.

Example:

import tensorflow as tf
print(tf.__version__)

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Question 2. Explain tensors in TensorFlow.

Tensors are multi-dimensional arrays, the fundamental building blocks of data in TensorFlow. They represent the input and output data of a computation graph.

Example:

tensor = tf.constant([1, 2, 3])
print(tensor)

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Question 3. What is a TensorFlow session and why is it important?

A TensorFlow session is an execution environment for running a computational graph. It encapsulates the state of the TensorFlow runtime and runs computational graphs.

Example:

with tf.Session() as sess:
result = sess.run(tensor)

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Question 4. Explain the concept of placeholders in TensorFlow.

Placeholders are used to feed actual training examples into the computational graph. They allow you to create the graph without knowing the actual data that will be fed into it.

Example:

x = tf.placeholder(tf.float32, shape=(None, input_size))

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Question 5. What is a TensorFlow variable and how is it different from a constant?

A TensorFlow variable is a mutable tensor that can be modified during program execution. Unlike constants, their values can be changed using operations like assign.

Example:

weight = tf.Variable(initial_value=tf.random_normal(shape=(input_size, output_size)))

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