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TensorFlow Pertanyaan dan Jawaban Wawancara

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TensorFlow vs PyTorch

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Ques 6. Explain the concept of eager execution in TensorFlow.

Eager execution is a mode in TensorFlow that allows operations to be executed immediately as they are called, rather than building a computational graph to execute later.

Example:

tf.compat.v1.enable_eager_execution()
result = tf.add(3, 5).numpy()

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Ques 7. What is a TensorFlow Estimator?

A TensorFlow Estimator is a high-level API for creating, training, and evaluating machine learning models. It simplifies the process of model development and deployment.

Example:

classifier = tf.estimator.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3)

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Ques 8. Explain the purpose of the tf.data module in TensorFlow.

The tf.data module provides a collection of classes for building efficient and scalable input pipelines for TensorFlow models. It is used to handle large datasets.

Example:

dataset = tf.data.Dataset.from_tensor_slices((features, labels))

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Ques 9. What is transfer learning, and how is it implemented in TensorFlow?

Transfer learning is a technique where a pre-trained model is used as the starting point for a new task. In TensorFlow, this can be achieved using the tf.keras.applications module.

Example:

base_model = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=False)

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Ques 10. Explain the concept of a TensorFlow Graph and Session in version 2.x.

In TensorFlow 2.x, eager execution is enabled by default, eliminating the need for explicit sessions and graphs. Computation is executed imperatively, as in regular Python code.

Example:

No explicit session or graph code is required in TensorFlow 2.x

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