This post is a collection of hypothesis test methodologies. The full collection is listed here: http://www.biostathandbook.com/testchoice.html. My post just goes over several hypothesis tests that are relevant to my research. One-way ANOVA: http://www.biostathandbook.com/onewayanova.html If you have one measurement variable and one nominal variable, and the nominal variable separates subjects into multiple groups, you want to test …
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I’ve been scratching my head for a day (06/24/2015) but it ended up that I am still baffled by the concept of Factor Analysis. So first of all, let me list what I’ve learned so far today. PCA and Factor Analysis can be deemed similar from the perspective that they are both dimension reduction technologies. …
Long Short Term Memory is claimed to be capable of predicting time series when there are long time lags of unknown sizes between important events. However, as to 2015.6, not many clear tutorials have been found on the Internet. I am going to list a collection of materials I came across. Probably I will write …
Update 07/15/2015: First, we should know how to calculate covariance matrix and correlation matrix. According to the wikipedia page, we have the calculation procedure of covariance matrix as: And the calculation procedure of correlation matrix is: I am going to use an R example to illustrate the calculation procedures. Let’s say we have a …
I’ve talked about the vanishing gradient problem in one old post in normal multiple layer neural networks. Pascanur et al. (the first in References below) particularly discussed the vanishing gradient problem as well as another type of gradient instable issue, the exploding gradient problem in the scope of recurrent neural network. Let’s recap the …
Andrew Ng Tutorial: https://web.stanford.edu/class/cs294a/sparseAutoencoder_2011new.pdf UFLDL Exercise: http://ufldl.stanford.edu/wiki/index.php/Exercise:Sparse_Autoencoder A Chinese blog: http://www.cnblogs.com/tornadomeet/archive/2013/03/20/2970724.html Stacked Denoising Autoencoder (paper): http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf
I am going to demonstrate a simple version of convolutional neural network, a type of deep learning structure in this post. Motivation Knowing normal multi-layer neural network (probably the one with input layer, output layer and one hidden layer) is helpful before you proceed reading this post. A good tutorial of MLN can be found …
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Background This post reviews how we update weights using the back propagation approach in a neural network. The goal of the review is to illustrate a notorious phenomenon in training MLNN, called “gradient vanish”. Start Let’s suppose that we have a very simple NN structure, with only one unit in each hidden layer, input layer …
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Maybe it sounds simple to computer scientists, I just want to backup some logical deduction of myself in NP-completeness proofs in case I will deal with such proofs in the future. In NP-completeness proof, we always want to find a polynomial-time reduction (transformation) from problem A to problem B, denoted as: We say that there …
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