Regression with Automatic Differentiation in TensorFlow

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In this Guided Project, you will:

Understanding tensor constants, and tensor variables in TensorFlow.

Understanding automatic differentiation in TensorFlow.

Using automatic differentiation to solve a linear regression problem in TensorFlow.

Clock1.5 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Mathematical OptimizationMachine LearningTensorflowLinear RegressionAutomatic Differentiation

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Tensor Constants

  2. Tensor Variables

  3. Automatic Differentiation

  4. Watching Tensors

  5. Persistent Tape

  6. Generating Data for Linear Regression

  7. Linear Regression

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.