Build a Deep Learning Based Image Classifier with R

4.6
stars
52 ratings
14 reviews
Offered By
Coursera Project Network
In this Guided Project, you will:

Solve a basic image classification problem with neural networks

Build, train, and evaluate a neural network model with Keras using R

Clock1 hour
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 45-min guided project, you will learn the basics of using the Keras interface to R with Tensorflow as its backend to solve an image classification problem. By the time you complete this project, you will have used the R programming language to build, train, and evaluate a neural network model to classify images of clothing items into categories such as t-shirts, trousers, and sneakers. We will be training the deep learning based image classification model on the Fashion MNIST dataset which contains 70000 grayscale images of clothes across 10 categories. In order to be successful in this project, you should be familiar with R programming, and basics of neural networks. 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

Deep LearningArtificial Neural NetworkMachine LearningTensorflowkeras

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. Project Overview and Import Libraries

  2. Import the Fashion MNIST Dataset

  3. Data Exploration

  4. Preprocess the Data

  5. Build the Model

  6. Compile the Model

  7. Train and Evaluate the Model

  8. Make Predictions on Test Data

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

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  • Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

  • You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.

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