Deep Learning Series 1: Classifying MNIST digits using Logistic Regression

Deep Learning Series 2: Classifying MNIST digits using Multi-Layer Perceptron

Deep Learning Series 3: Convolutional Neural Networks (LeNet)

Deep Learning Series 4: Stacked Denoising Autoencoders (SdA)

Summary:

In my exercise, classifying MNIST digits using Logistic Regression, have best test error 7.5%, but training the model only spend 20.4 second in my laptop on CPU.

Classifying MNIST digits get best test error 1.65% by using Multi-Layer Perceptron, but training the model spend nearly six hours in the same computer on CPU.

Tiny improve in test error, need more capacity or knoweledge and time.

No free lunch.

Using CNN to classifying MNIST digits will get test performance 0.92%.

Welcome your advice and suggestion!

Just record, this article was posted at linkedin, and have 121 views to November 2021.