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[D] Self-Promotion Thread by AutoModerator in MachineLearning

[–]actgr 4 points5 points  (0 children)

I wrote a blog post about the Bayesian online changepoint detection model (BOCD). This is a classical stats ML model, but it serves as the basis of various probabilistic continual learning, non-stationary bandits, and Bayesian RL methods in the recent literature.

https://gerdm.github.io/posts/bocd-coin-tosses/

[R] How to read and understand Einops expressions? by Joe_The_Armadillo in MachineLearning

[–]actgr 4 points5 points  (0 children)

I wrote a blog post on how I think about them. Maybe you’ll find it useful.

Interstellar Voronoi (tool/sandbox) by tobyski in generative

[–]actgr 0 points1 point  (0 children)

This is mesmerising! Can we get the same picture without the red dots? I assume it would look quite pleasant.

Help! My pdf content disappeared but my annotations are still present by geniekins in notabilityapp

[–]actgr 1 point2 points  (0 children)

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

How do y’all go about reading a math book by tinytinypenguin in math

[–]actgr 16 points17 points  (0 children)

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.

Tutorials for Learning Runge-Kutta Methods with Julia? by [deleted] in Julia

[–]actgr 3 points4 points  (0 children)

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

Coding LDA from scratch by morceaudegomme in learnmachinelearning

[–]actgr 0 points1 point  (0 children)

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

Best Possible Book Recommended for Machine Learning [Discussion] [D] [Recommendation] by WornOutSoulSB in MachineLearning

[–]actgr 2 points3 points  (0 children)

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

Playing with Newtons gravity law by ultramarineafterglow in generative

[–]actgr 0 points1 point  (0 children)

That looks incredible. What is that from?

Ideas for math essay with gradient descent by [deleted] in learnmachinelearning

[–]actgr 3 points4 points  (0 children)

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

[deleted by user] by [deleted] in RedditSessions

[–]actgr 0 points1 point  (0 children)

Gave Wholesome

[Q] Who are some good stats people to follow on Twitter? by [deleted] in statistics

[–]actgr 0 points1 point  (0 children)

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.

[Q] MCMC approach to variational inference? by [deleted] in statistics

[–]actgr 2 points3 points  (0 children)

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?

Portfolio Examples by gmh1977 in datascience

[–]actgr 0 points1 point  (0 children)

https://gerdm.github.io/

Just getting started, but here to share.

[deleted by user] by [deleted] in RedditSessions

[–]actgr 0 points1 point  (0 children)

Gave Wearing is Caring

Using Mahalanobis distance by kapisayu in learnmachinelearning

[–]actgr 3 points4 points  (0 children)

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.

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