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.
Object Detection : faster R-CNN
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
Sentiment classification
Machine Translation
Above graphs come from cs231n slides or cs224d slides.
Enjoy your journey about deep learning!
Welcome your advice and suggestion!
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