Understanding Convolutional Neural Networks
poloclub.github.io
Understanding Convolutional Neural Networks
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Re: Understanding Convolutional Neural Networks
#2The interactive inspection of each layer is beautifully implemented. I hope that one day we'll be able to make even more sense of the consequence of each individual weight, ie. know more than -0.52 for a given pixel.
Re: Understanding Convolutional Neural Networks
#3[deleted]
Re: Understanding Convolutional Neural Networks
#4Very nice interactive tutorial tool. I wish some of the terms were defined. Hyperparameter? Convolution? Kernel?
Re: Understanding Convolutional Neural Networks
#5Very nice interactive tutorial tool. I wish some of the terms were defined. Hyperparameter? Convolution? Kernel?
Model: the learnt relationship (eg., f(x; a,b) = ax + b)
Parameter: an aspect of the model, a dial which is fixed by data (eg., a)
Kernel (as used here): a subset of such parameters
Algorithm: procedure which accepts data and produces a model
Hyperparameter: an aspect of the algorithm, a dial which changes model production
Convolution: A convolution of image A and Filter B describes to what degree A is "like" B. Here "Filter B" is a kernel, ie., a parameter set learnt by the network.
The goal of a CNN is to produce a model whose parameters are image filters that describe the degree to which an images expresses various shapes. By learning the filters from an image set, the network is specialized to distinguish images in that set.
Re: Understanding Convolutional Neural Networks
#6I know very little about CNNs. But, I noticed the ReLu Activation step is Max(0,x) where x is the sum of the pixel intensities from each channel. In this example, it appears x > 0 (for all x) and so the activation step isn't really doing much?
EDIT: I'm wrong. x < 0 for some of the pixels. Specifically for the more red-ish channels.
Re: Understanding Convolutional Neural Networks
#7Just a shout-out to Professor Polo (the Professor who is the leader of the Polo Club of Data Science who wrote the tutorial on CNN's), 3 years ago, as a CRUD code monkey, I found out about Georgia Tech's launching of Online Masters in Data Science on Hacker News and then entered into the program and took Dr.Polo's class on Data Visualization and Analysis (for that semester I lit. spent more time on the class than on my day-job)... now for going full circle 3 years later, I see Dr.Polo's tutorial on CNN on Hacker News again and am working on CNN/RNN's for my last class/capstone project lol. The circle of life or the circle of HN I suppose!
Anyone here in the OMSA/OMSCS program?
Re: Understanding Convolutional Neural Networks
#8I know very little about CNNs. But, I noticed the ReLu Activation step is Max(0,x) where x is the sum of the pixel intensities from each channel. In this example, it appears x > 0 (for all x) and so the activation step isn't really doing much? EDIT: I'm wrong. x < 0 for some of the pixels. Specifically for the more red-ish channels.
No, relu is applied after convolution, so x is the result of applying the kernel at a particular location of the input, so it depends on the color as well as on the kernel.
Re: Understanding Convolutional Neural Networks
#9Why doesn't it explain what the "bias" is?
Re: Understanding Convolutional Neural Networks
#10Why doesn't it explain what the "bias" is?
Bias is just a scalar term that is added. You can learn it via backpropagation like all the other weights.