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)

Deep Learning Series 5: Understand Backpropagation and Gradient Descent

Deep Learning Series 6: Classify hand-written digits by keras

In above article from 1 to 4, I use the package Theano to do some code, article 5 use the package numpy to do example code, article 6 use the package keras to make model.

Deep learning has many excited applications.

Let’s look at them.

  1. Computer Vision Task

Object Detection : faster R-CNN

  1. Art in Computer Vision

In this case we simply feed the network an arbitrary image or photo and let the network analyze the picture. We then pick a layer and ask the network to enhance whatever it detected.

This creates a feedback loop: if a cloud looks a little bit like a bird, the network will make it look more like a bird.

This in turn will make the network recognize the bird even more strongly on the next pass and so forth, until a highly detailed bird appears, seemingly out of nowhere.

This just like a scene in the movie modern time, a man whose job is to screw the screw, he worked hour by hour, day by day, someday, he is a little crazy, whatever he see, he see a screw.

https://github.com/google/deepdream

the content of the content images + the style of the style images

https://github.com/jcjohnson/neural-style

  1. Text Mining

Above graphs come from cs231n slides or cs224d slides.

Enjoy your journey about deep learning!

Welcome your advice and suggestion!

Just record, this article was posted at linkedin, and have 278 views to November 2021.