I'm reading Deep Learning Book and puzzled by this "undefined identifier" (the Pa in the image, line 4). It appears at Page 208. Can you tell me just what Pa() means? Just a tip so that I can refer to Google. Thanks a lot!
Link to origin image | I'm not allowed to post image directly
It means "Parents". The feed forward computation needs the values of the previous nodes to proceed.
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I am trying to do a paired sample t-test analysis with SPSS, but the column that should hold the two-tailed significance is split in two: "1 sided p" and "2 sided p". I do not know how to interpret this result. Please help me out.
I want to either get the result I am looking for, which is the two-tailed significance, or understand how to interpret the results I am getting, which is 1 sided p and 2 sided p. I'm not permitted to add images yet so here is a link to the report:
C:\Users\User\Documents\paired sample t test stack.png
Thank you in advance.
I tried to run the test about 4 times with different variables and tried clicking on other options before running the analysis but the result is the same.
I unfortunately don't have SPSS anymore and cannot see your link, but alas I have looked on YouTube and found a video that shows the output of a paired samples t-test for SPSS. Here is what they have and I have highlighted what I suspect your interpretation issue is:
Basically, SPSS by default gives you the result of a one-tail and two-tail test automatically without really saying which is "correct" (this is what "one-sided" and "two-sided" mean by the way). If you are only interested in testing if there is a significant difference in either direction (two-tailed), then you only use the two-tail test p value. So in your case, just ignore the "one-sided" p-value and use the "two-sided" p value instead.
I'm stuck with a problem statement of predicting an identifier for a product on the basis of couple of product features. A sample of data available to me looks like the one shown below:
ABC10L 20.0 34 XYZ G345F FG MKD -> 000000DEF_VYA
Here, ABC10L,20.0,34,XYZ,G345F,FG,MKD are the features and 000000DEF_VYA is the unique identifier associated with the product. Initially I tried to formulate this problem as a regression problem but I'm not sure how to generate textual output from my model and what should be my cost function. Also, I'm not sure is regression the right tool to solve the issue here.
Please help in suggesting the right approach and how I may proceed to solve this !!!
I am currently integrating the BERT model listed on https://developer.apple.com/machine-learning/models/#text into an iOS application and have had difficulty removing answers that have low certainty.
I have used the sample code found at the link above but because I wanted to answer questions based on larger volumes of text, I loop over an array of paragraphs and predict an answer for each one. However, the model does not return nil or "No Answer" if an answer is not found and instead returns a (seemingly) random substring. I suppose what I am trying to ask is: is it possible to access the certainty of BERT's response to filter out unlikely results? Or is there another way to get BERT to only return results above a set certainty threshold?
After hours of searching, I've now found a solution. Ironically it only took three lines of code, but here it is anyway:
if bestSum < 7.5 {
return nil
}
I implemented this in the findBestLogitPair() method in the BERTOutput.swift file as provided in Apple's sample code for text analysis using BERT. I have now discovered that the word logit does kind of mean probability in statistics - but being a programmer, I had no idea!
I am using core nlp library to find coreference in my text
Tyson lives in New York City with his wife and their two children.
when I am running this on Stanford CoreNLP Online demo it's giving me correct output
but when I run this text on my machine it's returning null on this line of code
Map graph = document.get(CorefChainAnnotation.class);
Thank you
Look into this complete example - http://blog.pengyifan.com/resolve-coreference-using-stanford-corenlp/. I guess you are missing something as i am unable to understand the exact reason from the code you provided.
Recently i am working on machine learning and build some Models for classification problem with the help of some tutorials. Though i solved my problem successfully but cant get the use and inference of using "NumerictoNominal" method please explain me.
I have tried to learn from the available text but it is very hard core i am seeking for simple explanation.
thanks and regards
I search a lot and finally got a simple example "A set of data is said to be nominal if the values / observations belonging to it can be assigned a code in the form of a number where the numbers are simply labels" for example PIN CODE of a City. Although we use Numeric value to build codes and also u can apply simple Algebra on PIN Codes but it won't make any sense. Also attribute SEX could be male or female it is also a kind of nominal attribute.
thanks