Model saving

04 Jan, 2025

Most Frequently asked Interview Questions of tensorflow(2024)

TensorFlow Serving is an open-source library designed for serving machine learning models in production environments. It is built specifically for serving TensorFlow models, but it can also be extended to serve models created with other machine learning frameworks. TensorFlow Serving simplifies the process of deploying models for inference, allowing you to expose models as APIs and serve them at scale.

04 Jan, 2025

Most Frequently asked Interview Questions of tensorflow

In TensorFlow, optimization refers to the process of adjusting the parameters (weights) of a machine learning model to minimize the loss function during training. The choice of optimizer can significantly affect the model’s performance, convergence speed, and stability. TensorFlow provides several optimizers, each with unique properties and use cases.

04 Jan, 2025

Most Frequently asked tensorflow Interview Questions (2024)

Managing large datasets in TensorFlow for training deep learning models involves several strategies to ensure that the model can efficiently process the data without running into memory issues or performance bottlenecks. Below are some common methods:

04 Jan, 2025

Most Frequently asked tensorflow Interview Questions and Answers

Defining and training a neural network in TensorFlow involves several key steps: preparing the dataset, defining the model architecture, compiling the model, training the model, and evaluating the model’s performance. In TensorFlow 2.x, this is typically done using the Keras API, which is integrated into TensorFlow as the high-level interface for building and training models.

04 Jan, 2025

Most Frequently asked tensorflow Interview Questions

#### What is TensorFlow?