Predictive Analytics with TensorFlow 3.2: TensorFlow Computational Graph

The video tutorial explains the execution of TensorFlow programs, focusing on graph creation and session execution. It highlights the role of TensorFlow's C++ engine, which handles operations like convolution and derivatives. The tutorial describes the computational graph, its nodes, and edges, including control dependencies. It also covers TensorFlow's main components: variables, tensors, placeholders, and sessions. The video concludes with an overview of the TensorFlow programming model, emphasizing the distribution of workloads across CPUs and GPUs.

What is the primary role of Python in TensorFlow execution?

• To perform all computations

• To serve as a wrapper for the C++ engine

• To execute operations directly

• To manage memory allocation

In a computational graph, what do the normal edges represent?

• Memory allocation

• Execution order

• Data structures between nodes

• Control dependencies

What is the purpose of control dependencies in TensorFlow?

• To increase data flow

• To enhance computational speed

• To simplify graph creation

• To enforce operation order and manage memory usage

Which component in TensorFlow is used to pass data between nodes?

• Variables

• Placeholders

• Tensors

• Sessions

How does TensorFlow decide whether to use a CPU or GPU?

• It requires manual selection by the user

• It always uses a GPU

• It automatically selects based on availability

• It always uses a CPU