Sample Size estimation paper

Very nice reference for SSD.

https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-12-8

Step-By-Step Framework for Imbalanced Classification Projects

https://machinelearningmastery.com/framework-for-imbalanced-classification-projects/ 

A link.

A Small collection on the ROC curve analysis

Here is a list of links i have found useful regarding the ROC curve.

This is a very readable link on ROC curve and selection of operating point.

Just today, i saw that Tom Fawcett had died in 2020 of a freak bicycle accident. A tribute article from his collaborator.

The classical article by Tom Fawcett here and here. For the latter article, one has to pay for it. But a search on any search engine may lead to other sources of information.

Redhat article onci/cd

https://www.redhat.com/en/topics/devops/what-is-ci-cd

Nice summarisation.

Links on git branching

Feature Branch workflow:

https://nvie.com/posts/a-successful-git-branching-model/

from this link which discusses different git WF

https://medium.com/flexisaf/git-workflow-for-your-project-3d9dbdc5f8e2

A capsule of ML metrics

Handy bookmark

https://towardsdatascience.com/the-ultimate-guide-to-binary-classification-metrics-c25c3627dd0a#0675

Testing from email

This is a test post from the email.

and here is a link.

https://github.com/tensorflow/tensorflow/issues/14857

Cloud from aeroplane window

Here is a photo i took from a recent trip from HKG->BLR; not that the matters because you cannot identify from the photo itself. The clouds look cool; i thought.

Book list

Here is my book-list. I plan to keep this updated with suggestions. I can read Hindi, English, Bengali and Tamil. If you like to make suggestions, please drop a comment.

Caffe using anaconda or miniconda

I have been spending some time in installing the machine learning software caffe on Slackware. I have a modest GPU card, the GE Force GT 730. Slackware version is 14.1. For installing caffe, there are a few requisites which can be easily installed from source. These are: gflags, glog, leveldb, lmbd, snappy and protobuf. Also, users of Python scientific computing packages would be aware of Anaconda or miniconda, which offers a bunch of scientific computing software(and maybe much more besides) in one package or an installer. This is a Very Useful Thing. However, I have had massive trouble compiling caffe using the Anaconda python. The only reason is that the final linking step throws up this kind of error . That is, libboost_python looks for a different version of python that provided by Anaconda(which we specified in the Makefile.config file prior to starting the build. One solution is to re-build the entire boost using the Anaconda python distribution, but it could mean making more changes systemwide. However, I do with that it would be easy to know which python version the built libboost_python.so links to. This information is not available from ldd. There is also an explanation to this in some thread, but practically I found that it is best to use the system python. With the availability of pip, it is no more tedious to install multiple packages.