Predictive Analytics with TensorFlow 3.4: Data Model in TensorFlow

The video tutorial introduces TensorFlow's data model, focusing on tensors, their properties, and operations. It covers creating tensors, understanding their rank and shape, and managing sessions. The tutorial also explains variables, fetches, and methods to input data into TensorFlow, preparing viewers for more advanced topics like linear regression.

What is a tensor in the context of TensorFlow?

• A simple scalar value

• A single-dimensional array

• A multidimensional numerical array

• A complex mathematical function

How is the rank of a tensor defined?

• By the size of its largest dimension

• By the number of dimensions it has

• By the number of elements it contains

• By the type of data it holds

What does the shape of a tensor represent?

• The rank of the tensor

• The data type of the tensor

• The number of rows and columns in the tensor

• The number of elements in the tensor

Which function is used to create a tensor with a constant value?

• TF.constant

• TF.variable

• TF.convert_to_tensor

• TF.placeholder

What is the purpose of using an interactive session in TensorFlow?

• To automatically optimize tensor operations

• To simplify the process of evaluating tensors in interactive environments

• To run TensorFlow code in a web browser

• To visualize tensor data in real-time

How are variables initialized in TensorFlow?

• Using the TF.variable function

• Using the TF.placeholder function

• Using the TF.constant function

• Using the TF.convert_to_tensor function

Which method is NOT used to feed data into a TensorFlow program?

• Tensor optimizer

• Preloaded data

• Queue runner

• Data set API