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Does Imagenet contain unlabeled examples like STL-10 dataset? because I see that the dataset is used for unsupervised learning but it seems to me it has only labeled examples.
You can parse the ImageNet data using ImageFolder in Pytorch. For unsupervised learning, simply don't account for the labels coming from the Dataloader
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I made a model for clustering and it's encoded dimension is about 3000. To check if the autoencoder is well established, I draw a 2d_pca plot and 3d_pca and the plots look nice.
My question is that, what is general way to cluster with this encoded features?
I think about some options:
First: to use all encoded features.
Second: to use all encoded pca features.
Third: to use some encoded pca features explaining almost 70% variance.
I think usual papers don't refer to it.
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Im still not sure how clustering can be used for predictive analytics?
can someone tell me how to predict the future from extracting clusters?
generally, clustering isn't used for prediction but for labeling or analyzing existing set of data points.
after you use clusters to label your data points and divide them into groups based on common traits, you can run other prediction algorithms on that labeled data to get predictions.
I don't think clustering leads directly to predictions, other than cases of clusters that are well separated and can be used to make inferences about the data points and the properties of the clusters
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While solving a puzzle, I ended up having a complexity of T(n)=T(n-3)+T(n-5).
I was trying subtraction method. But I am unable to solve this. Please explain what should be the procedure.
This is a linear homogeneous difference equation with constant coeffs.. It is usually solved by transforming it to the complex plane and solving a polynomial.
Without a CS background (as you state), I'm afraid the details wouldn't fit in here. Start with the Wikipedia entry, if you're interested.
If you want to skip to the final solution, here is the Wolfram Alpha for it.
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I have a text file with the values stored as a single column. I need to import the data into matlab as a 3d matrix. How to do so?
Import them as a 1D matrix and then use reshape.
dlmread might help you with the import
and if your data is row-major, then you will want to use permute at the end.
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I need to design a neural network which has the following behavior:
p(1)={0,1,1,1} outputs a(1)={0,1,0,0}
p(2)={1,1,0,1} outputs a(2)={0,0,1,0}
p(3)={0,0,1,0} outputs a(3)={0,0,0,1}
p(4)={0,0,1,1} outputs a(4)={1,1,0,1}
How can i do so? Which type of neural network should I use? Which learning method can be used here?
Thanks.
At first glance it seems as though you could use a simple feedforward neural network with one input layer one, one hidden layer and one output layer. You can use your training data to train the neural network using the backpropogation algorithm.
See this page for more details:
http://en.wikipedia.org/wiki/Backpropagation