Machine Learning: Create a Neural Network that Predicts whether an Image is a Car or Airplane.

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En este proyecto guiado, tú:

  1. 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.

Clock2 Hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

Image ProcessingComputer VisionData AnalysisMachine Learning

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. 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.

  2. Task 2: The Learners will get practice Loading Images.

  3. Task 3: The Learner will get experience pre-processing images using the EBImage package in R.

  4. Task 4: The Learner will reshape the images using a Keras function called array_reshape().

  5. Task 5: The Learner will get practice creating Testing and Training sets.

  6. Task 6: The Learner will then create a classifier using one hot encoding.

  7. Task 7: The Learner will then build out the architecture for the Neural Network. Rectified Linear Unit ("RELU") and SoftMax will be used.

  8. Task 8: The Learner will then build out a loss optimizer function using cross_entropy.

  9. Task 9: The Learner will test to see how the model performed using a Confusion Matrix.

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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