Machine Learning: Create a Neural Network that Predicts whether an Image is a Car or Airplane.
- Complete a Neural Network Model that will be used to evaluate whether a Picture is an Airplane or Automobile.
Practice using One Hot Encoding to build a classifier.
Practice evaluating model performance.
In this 1-hour long project-based course, you will learn how to build a Neural Network Model using Keras and the MNIST Data Set. By the end of the course you will have built a model that will recognize the digits of hand written numbers. You will also be exposed to One Hot Encoding, Neural Network Architecture, Loss Optimizers and Testing of the Model's performance. 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
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:
Task 1: In this task the Learner will be introduced to the Course Objectives, which is to how to execute a Neural Network using raw images from the Internet.
Task 2: The Learners will get practice Loading Images.
Task 3: The Learner will get experience pre-processing images using the EBImage package in R.
Task 4: The Learner will reshape the images using a Keras function called array_reshape().
Task 5: The Learner will get practice creating Testing and Training sets.
Task 6: The Learner will then create a classifier using one hot encoding.
Task 7: The Learner will then build out the architecture for the Neural Network. Rectified Linear Unit ("RELU") and SoftMax will be used.
Task 8: The Learner will then build out a loss optimizer function using cross_entropy.
Task 9: The Learner will test to see how the model performed using a Confusion Matrix.
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In a split-screen video, your instructor guides you step-by-step
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