Hello, I’ve stumbled upon an extremely interesting dataset http://www2.informatik.uni-freiburg.de/~cziegler/BX/ regarding book recommendation engine. The dataset has been already uploaded on Kaggle but it’s not public. Thus, I’d like to share it here for those who want to try by themself or are open to co-operate on the project with me. I’ve already done some construct on collaborative filtering, content-based filtering, svd and matrix factorization, …

I’ve uploaded my notebook here: https://gofile.io/?c=YXL55u

Feel free to download or pm me for sharing our progress.

Cheers!

https://www.pycm.ir

https://github.com/sepandhaghighi/pycm

​

• Tversky index (TI) added #214
• Area under the PR curve (AUPR) added #216
• FUNDING.yml added
• AUC_calc function modified
• Document modified #225
• summary parameter added to save_html,save_stat,save_csv and stat methods #217
• sample_weight bug in numpy array format fixed #227
• Inputs manipulation bug fixed #226
• Test system modified #229
• Warning system modified #228
• alt_link parameter added to save_html method and online_help function #232
• Compare class tests moved to compare_test.py
• Warning tests moved to warning_test.py

I’m curious what ticket management software(if any) research labs are using. I’m especially curious about university research labs. My group is kicking around the idea of using one and I’m not sure if it makes that much sense for academic research.

Hi, I’m Myeongjun Kim. My major is Computer Vision using deep learning. “MixNet: Mixed Depthwise Convolutional Kernels” paper has been uploaded to the archive on 22 july 2019. This paper improves performance and efficiency by changing kernel size. I have implemented pytorch version with reference to official code implemented with tensorflow. Currently I’m experimenting with CIFAR-10 because of the lack of gpu.

Thank you for reading my story 🙂

[Github URL]: https://github.com/leaderj1001/Mixed-Depthwise-Convolutional-Kernels

As suggested by the title, we all know how frustrating it could be to get a paper published in very popular journals, not only they have a poor quality of reviews, but also due to the fact that a LOT of scavengers haunt these places. These people, all they want is credit, and fame from the works of others.

Not only this, these guys will literally stalk you of your academic research just to get that subtle hint of [this guy’s work might be related to mine, as well as accuse him of “paper is not cited by you”] . I mean common guys, this is a place where science is to be developed, not to get on each others throat for some petty gains.

My next argument is the shit qualities of reviewers on the research paper conferences (I will not name them). Reviewers literally point out issues that are trivial, and aimed to delay the acceptance date of the paper. They will post poor quality of reviews on papers, some will go even further to flame the researcher and start an argument.

This scenario is so fucked up, demotivates me to core, and makes me never want to submit a paper to these publications/conferences.

Hello r/MachineLearning ~

Struggling Noob here. I have been asked to create a pls model on a dataset with 200 predictor variables and 1 response variable(y below). I have only 16 observations. I know the n is small and predictors are high, but these are outside of my control.

I have made a model, with the code below, but I have little confidence it is right. I don’t know how to properly test it. The summary says after 2 components, 95% is explained.

My Questions:

1. How do I see what variables are in those components.
2. How do I test the model pls.fit or determine the R^2

Thank you so much for any help. If anyone has strong knowledge here, I’d be happy to pay for a quick tutoring session.

pls.fit <- plsr(df$y~.,data = df, scale = TRUE,validation = "LOO")

summary(pls.fit)

validationplot(pls.fit)

Hey fellow Reddit Machine Learners. At what point do you guys transition your projects from working locally on your laptops/desktops to developing directly on the cloud? I find the transition not particularly smooth. I am curious about your experience. Have you considered starting directly on a cloud machine?