Mathematics

The third part covers adjoint functors, diagram shapes and categories, cones and cocones, limits and colimits, pullbacks and pushouts.

This book is actually a cheat sheet about computing matrix algebra operations such as matrix multiplication, inversion and factorization.It is written foR (aspiring) data scientists where with "foR" (capital letter R) I mean the side of data science addicted to R and its gorgeous ecosystem especially including Rcpp, RcppArmadillo and RcppEigen.

This machine learning book series aims at providing real hands-on training from general concepts and architecture to low-level details and mathematics. The first epoch covers the simplest linear associative network, proposes a brick notation for algebraic expressions, shows required calculus derivations, and illustrates gradient descent.

An algebraic axiomatic theory of formulas for mathematicians and programmers.

Was a spell laid to Earth mathematicians that the most important concept in general topology was discovered only in 2019?

A generalization of general topology expressing topological properties with algebraic formulas. Even beginning math students can read.

The second part covers duality, products, coproducts, biproducts, initial and terminal objects, pointed categories, matrix representation of morphisms, and monoids.

Category theory abstractions are very challenging to apprehend correctly, require a steep learning curve for non-mathematicians, and, for people with traditional naïve set theory education, a paradigm shift in thinking. The book uses LEGO® to teach category theory.

Lastly, there is no such thing as Hawking Radiation. Quantum Gravity takes into accountability the forces of black holes, anti - radiation and energy that follows as a result of the interaction of three of the four forces of nature.

These three-volume book series cover a wide variety of topics in machine learning focusing on supervised and unsupervised learning, intended for data scientist and machine learning experts providing a very concise description of the scikit-learn library. The first volume covers the generalized linear models (linear & logistic regression).

General trace and log analysis patterns allow the application of uniform diagnostics and anomaly detection across diverse software environments. This pattern language covers any execution artifact from a small debugging trace to a distributed log with billions of messages from hundreds of computers, thousands of components, threads, and processes.