Part 1: “Homology” without Pre-requisites, except “Function” (he un-rigourously interchanges with “Mapping”, although Function is stricter with 1-and-only-1 Image) .

Part 2: Simplex (单纯形)

Topology History : Euler Characteristic eg. (V – E + R = 2) , Poincaré invention

This video uses Algebra of point, line, triangle… to explain a Simplex (plural: Simplices) in R^{n} Space, that is organizing the n-Dimensional “Big Data” data points into Simplices, then (future Part 3, 4…) compute the “holes” (or pattern called Persistent Homology).

Part 3: Boundary

Part 3 justifies why triangles (formed by any 3 data points) called “Simplex” 单纯形(plural: Simplices) are best to fill any Big Data Space.

“Algebraic Topology” can detect the Multi-dimensional neural network in our brain – by studying the Homology (同调) and co-Homology (上同调) with the help of Linear Algebra (multi-dim Matrix) & Computers.

Homology = compute the number of “holes” in multi-dim space.

Neurons formed in the brain can be modeled in Math (Topology) by Simplex 单纯 (plural : Simplices), billions of them interconnected into a complex – “Simplicial Complex” (单纯复体)。

Vector Space (to + – , × ÷ by multiplier Field scalars);

Ring (to + x) in co-homology

etc.

then apply algebra (Linear Algebra, Matrices) with computer to compute these invariants (homology, co-homology, etc).

A topological space can be formed by a “Big Data” Point Set, e.g. genes, tumors, drugs, images, graphics, etc. By finding (co)- / homology – hence the intuitive Chinese term (上) /同调 [2] – is to find “holes” in the Big Data in the 10,000 (e.g.) dimensional space the hidden information (co-relationship, patterns, etc). Note: [1] Analogy of an”Invariant” in Population: eg. “Age” is an invariant can be added in the “Population Space” as the average age of the citizens.

Homology and co-homology are the Top 10 Toughest Math in the world (1st & 8th topics in the list, of which 3rd, 4th, 9th and 10th topics received the Fields Medals). Like most Math concepts, which were discovered few decades or centuries ago, now become useful in scientific / industrial / computer applications never thought before by their discoverers.

Examples: Prime numbers, Chinese Remainder Theorem 韩信点兵 / Fermat’s Little Theorem / Gauss Modular Arithmetic / Elliptic Curve in Cryptography; Homology in Big Data Analytics for Epidemic Medicine, Pharmaceutical Drugs, Consumer Behavior Study, Stock Market, Economy, etc.

This Homology Fundamental Equation puzzles most people. WHY ?

It is analogous to the Vector Algebra:
Let the boundary of {A, B} =