Machine Learning: Predict Numbers from Handwritten Digits using a Neural Network, Keras, and R

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在此指导项目中,您将:

Train and Test a Neural Network Model to read hand written numbers and return the digit.

Practice using One Hot Encoding to build a classifier.

Practice evaluating model performance.

Clock2 Hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

Artificial Neural NetworkAnalyticsMachine Learning

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Task 1: In this task the Learner will be introduced to the Course Objectives, which is to how to execute a Neural Network on the MNIST Data Set. There will also be a short discussion about the Interface, loading packages, and an Instructor Bio.

  2. Task 2: The Learners will see what a Tensor looks like and then apply that knowledge to 60,000 hand written digits using Keras array_reshape() function.

  3. Task 3: The Learner will then create a classifier using one hot encoding.

  4. Task 4: The Learner will then build out the architecture for the Neural Network. Rectified Linear Unit ("RELU") and SoftMax will be used.

  5. Task 5: The Learner will then build out a loss optimizer function using cross_entropy.

  6. Task 6: The Learner will test to see how the model performed using a Confusion Matrix.Task 3: The Learner will get experience creating Testing and Training Data Sets. There are multiple ways to do this and the Instructor will go over two of them in this Task.

指导项目工作原理

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