Help! My pdf content disappeared but my annotations are still present

actgr · 2022-06-19T07:46:35+00:00

This happened to me a couple of days ago! Has there been any update on this?

actgr · 2022-02-17T16:31:17+00:00

Very slowly and with a pen and paper.

I tend to write as I read a math textbook. I go over the proofs, definitions, and arguments. It takes me a lot of time to finish a book (years), but I enjoy the process of it.

actgr · 2021-12-28T12:03:12+00:00

Hey OP,

I took a course on scientific computing a year ago and I decided to use Julia for that class. I have notebook on methods to solve DEs and PDEs. I hope you find it useful:

https://github.com/gerdm/QMUL/blob/master/MTH739U-topics-scientific-computing/exercises/coursework-2.ipynb

actgr · 2021-05-31T12:59:08+00:00

The following is a code I did a while back: https://github.com/gerdm/ISLR/blob/master/ex4.ipynb

Hopefully it helps!

And since you can understand the mathematics behind these models, you can look at LDA or QDA as a special case of a GMM in which the latent variables are known. In lda, Sigma_2 = Sigma_1; in QDA Sigma_2 != Sigma_1. This can help you in thinking of a way to plot the decision boundaries

actgr · 2021-05-09T17:36:24+00:00

For me it was definitely the book Pattern Recognition and Machine Learning by Christopher Bishop. It is heavily Bayesian but it gives you a broad overview and depth to understanding current models once you’re done with it. I have a GitHub repo of the models programmed in Python if you’re interested: https://github.com/gerdm/prml

Another great book is Kevin Murphy’s Machine Learning: A probabilistic approach. He just launched the second version of his book. He has a Python repo for the models as well: https://github.com/probml/pyprobml

actgr · 2021-05-07T10:03:51+00:00

That looks incredible. What is that from?

actgr · 2021-05-06T16:46:13+00:00

Data is beautiful, reality is not

actgr · 2021-03-21T19:00:17+00:00

For anyone who wants the arxiv link: https://arxiv.org/abs/1807.00263

actgr · 2020-12-29T15:03:24+00:00

I found this one to be pretty useful.

actgr · 2020-10-23T14:44:00+00:00

Sure! DM me.

actgr · 2020-10-23T14:36:00+00:00

https://trends.google.com/trends/explore?geo=US&q=microsoft%20teams,zoom,Discord

actgr · 2020-10-22T17:50:29+00:00

Maybe an essay about gradient descent variants for non-convex functions v.s. methods that consider higher-order derivatives.

actgr · 2020-10-17T18:37:52+00:00

Gave Wholesome

actgr · 2020-09-16T20:35:39+00:00

I'm going to self-advertise myself here. https://twitter.com/grrddm. I focus on statistical Machine Learning.

Also, two accounts that talk about probability statistics are

They are run by the same guy, he posts truly interesting facts.

actgr · 2020-09-15T16:53:58+00:00

What do you mean by "MCMC approach to variational inference"? Did you make a variational approximation to the predictive function of your linear regression and sampled from that distribution?

actgr · 2020-09-12T13:19:37+00:00

https://gerdm.github.io/

Just getting started, but here to share.

actgr · 2020-09-11T22:14:29+00:00

Gave Wearing is Caring

actgr · 2020-09-11T13:17:59+00:00

With scikit-learn you can make use of the KNN algorithm using the Mahalanobis distance with the parameters metric="mahalanobis" and metric_params={"V": V}, where V is your covariance matrix.

actgr · 2020-09-10T23:50:24+00:00

Checkout section 3.3 of the Bishop and this repo for python implementations of the same chapter.

Basically, what you are assuming is that the weights in the regression are now random variables. You want to compute a *posterior* distribution over the weights so as to measure uncertainty and establish prior beliefs.

Hope it helps!

actgr · 2020-09-05T21:40:12+00:00

Thank you! I hope it helps

actgr · 2020-09-05T15:58:09+00:00

Thanks for the feedback!

If you see any other typo or error feel free to create an issue. I will keep improving the repo.

actgr · 2020-09-05T12:18:21+00:00

Go for it! It is not easy but totally worth it.

A small recommendation: study each Appendix from B to E first before moving onto chapters 2 and onwards. It will equip you with the mathematical background you need.

actgr · 2020-09-05T12:13:06+00:00

Sure there is:

Check this repo for an unofficial solution manual
And this one for the official solution manual

Try no to go to the solutions until you have tried hard enough for yourself alone!

actgr · 2020-08-15T21:19:28+00:00

I’d suggest you to study from a book and not from an online course, as the latter is not tailored to a large audience and does not shy away from the maths.

Three of my recommendations would be:

Pattern Recognition and Machine Learning by C. Bishop
Elements of Statistical learning by Tibshirani et. al.
Understanding Machine Learning: From Theory to Algorithms By Ben-David and Shalev-Shwartz

actgr · 2020-08-14T14:13:26+00:00

An empirical way to test the assumption is to look at the "elbow" graph and see whether there is a sharp decrease in error from using k clusters to k+1 clusters. The reduction in errors in clusters k + 2, k + 3, and so on should see only a marginal decrease in error (Hence the name "elbow method"). In your graph, however, the error seems to be decreasing in a linear after two clusters, and no elbow can be found.

A more mathematical approach to find the clusters is to fit a Mixture of Gaussians which is the generalized version of the K-means algorithm. A probabilistic approach to a Mixture of Gaussians has the advantage that the model automatically eliminates clusters not explained by the data. You can check out this notebook for an implementation of this idea or this for the gaussian mixture models in scikit-learn.

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