Understanding tensor constants, and tensor variables in TensorFlow.
Understanding automatic differentiation in TensorFlow.
Using automatic differentiation to solve a linear regression problem in TensorFlow.
In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem. By the end of this project, you will have a good understanding of how machine learning algorithms can be implemented in TensorFlow.
In order to be successful in this project, you should be familiar with Python, Gradient Descent, Linear Regression.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Regression with Automatic Differentiation in TensorFlow
Welcome to Regression with Automatic Differentiation in TensorFlow. In this project, we will get started with some of the important basics of TensorFlow - like tensor constants, variables, and automatic differentiation. We will then apply this knowledge to solve a linear regression problem. By the end of the project, you will have a good understanding on how to approach implementing machine learning algorithms in TensorFlow.
Generating Data for Linear Regression