Memo's Island

Preamble The idea of generating randomness via computers goes back to von Neumann's and his team of colleagues work in Princeton and Los Alamos.

Preamble Image: Babylonian table for computation (Wikipedia) Probably, one of the most important aspects of computational work in quantitative fields, such as physics and data sciences is stability of numerical computations.

Rubik's cube (Wikipedia) Preamble Counting is one of the most important concepts in probability and statistical mechanics as well.

Extension of this post, appear as a short article with a conjecture: Mehmet Süzen.

Preamble Probably, one of the most fundamental issue in classical statistical mechanics is extending reversible dynamics to many-particle systems that behaves irreversibly.

Preamble Figure: Visual description of random stream chunking, M.Suzen (2017) The advent of using computational sciences approaches, i.e., data science and machine learning, in the industry becomes more common practice in almost all organisations due to t…

Roulette Wheel (Wikipedia) Preamble In this short exposition, we inquire about the purpose of randomness and how this related to discovering or testing causal inferential problem solving using data and causal models.

Dominos in motion (Wikipedia) Preamble The definition of weighted directed graphs (wDAGs) provides a great opportunity to express causal relationships among given variates.

Preamble The entropy is probably one of the most difficult physical concepts to grasp.

Preamble Fractal Tree (Wikipedia) A quiet causal inference revolution is underway in industry.

Kindly reposted to KDnuggets by Gregory Piatetsky-Shapiro Preamble Time-series analysis is needed almost in any quantitative field and real-life systems that collects data over time, i.e., temporal datasets.

Domino OR-gate (Wikipedia) Preamble A trained model may provide predictions about input values it may never seen before but it isn't an inference, at least for 'classical' supervised learning.

Summary Problem-solving is the core activity of data science using scientific principles and evidence.

Preamble A recently shown phenomenon can classify deep learning architectures with only using the knowledge gained by trained weights [suezen20a].

Preamble Catalan Castellers are collaborating (Wikipedia) Availability of distributed code tracking tools and associated collaborative tools make life much easier in building collaborative scientific tools and products.

Preamble Artificial Intelligence Engines: An introduction to the Mathematics of Deep Learning by Dr James V.

Summary Many data scientist are trained or experienced in using tools to do statistical modelling, forecasting or machine learning solutions, this doesn't necessarily mean that they should just jump out and do an ad-hoc analysis on the available public da…

Kindly reposted to KDnuggets by Gregory Piatetsky-Shapiro Preamble The core application of machine learning models is a binary classification task.

Kindly reposted to KDnuggets by Gregory Piatetsky-Shapiro with the title Applying Occam's razor to Deep Learning Kindly reviewed by Cornelius Weber Preamble: Changing concepts in machine learning due to deep learning Occam's razor or principle of parsimony

Fibonacci, Leonardo da Pisa (Wikipedia) Updated with memoization on 26 Feb 2020 Preamble The Fibonacci sequence $F_{n} = F_{n-1} + F_{n-2}$ is famous for both artistic and pure mathematical curiosity as an integer sequence.

As mentioned by Professor Pearl graciously on twitter Preamble Hubble Space Telescope (Wikipedia) Computational science is to modern data scientist, as telescopes are for astrophysics.

Preamble One of the main tenants of practical data science is performing statistical inference on data sets which are assumed to be representations of populations of activity, natural or man-made.

Kindly reposto to KDnuggets by Gregory Piatetsky-Shapiro Preamble Almost all businesses and industry embraced Machine learning (ML) technologies.

Preamble Ebbinghaus (Wikipedia Machine Learning (ML) is now a de-facto skill for every quantitative job and almost every industry embraced it, even though fundamentals of the field is not new at all.

Kindly reposted to KDnuggets by Gregory Piatetsky-Shapiro with the title Understanding overfitting: an inaccurate meme in machine learning Preamble There is a lot of confusion among practitioners regarding the concept of overfitting.