Predictive Analytics with TensorFlow 3.5: Getting Started with Tensorflow – Linear Regression and Beyond

This video tutorial introduces linear regression in the context of TensorFlow, explaining its use in measuring relationships between variables. It covers generating data using Python, building a linear regression model, and optimizing it using gradient descent. The tutorial demonstrates the iterative process of finding optimal parameter values and concludes with the results of the model's performance.

What is the primary objective of implementing linear regression in TensorFlow?

Explain the concept of a cost function in the context of linear regression.

Describe the gradient descent algorithm and its role in optimizing parameters.

What are the initial values of W and b in the algorithm, and what does it indicate about the loss?

How does the algorithm adjust the values of W and b during the iterations?