Interview Questions and Answers

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.

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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.

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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.

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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.

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Adam is an optimization algorithm commonly used for training deep learning models. It adapts the learning rates of each parameter individually and combines the advantages of other optimization methods.

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tf.distribute.Strategy is used for distributed training in TensorFlow. It allows you to efficiently train models across multiple GPUs or devices, improving training speed and scalability.

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The input_signature parameter in tf.function specifies the input signature of the function. It helps in static shape checking, allowing TensorFlow to optimize the function for a specific input signature.

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