Cs-Theory on The VikiD Truth

Algorithms

Mon, 08 May 2017 05:00:32 +0400

Algorithms

Here is the truth about algorithms: they are recipes, and the interesting question is not “does it work?” but “how does the cost grow when the input gets bigger?” Once you start seeing growth rates, a lot of software design stops being guesswork.

Machine Learning

Thu, 01 Dec 2016 22:59:57 +0400

Machine Learning

Machine learning in a Nutshell:

Data: Raw Environment
Model: Maps input –> Output
Learning: Optimizes Models by finding better parameters to models (patterns).
Predictor: A MLA that is a system that makes predictions
We Represent Data as Vectors
Choose a Model
Optimize using numerical methods

The mathematics needed:

Linear Algebra
- Systems of Equations
  - Matrices
  - Vector Spaces
  - Linear Mappings
  - Affine Spaces
Analytic Geometry
Matrix Decomposition
Vector Calculus
Probability Distributions
Continuous Optimisation
Models vs Data
Linear Regression
Dimensionality
Principal Component Analysis
Density Estimation (Gaussian Mixture Models
Classification with Support Vector Machines

Sources, Links and Reading Lists

Databases

Wed, 30 Nov 2016 07:56:19 +0400

Databases

Here is the truth about databases: persistence is hard, and “just write it to a file” becomes a nightmare the moment two writers, a crash or a query show up. A database is the carefully engineered answer to those three problems.

Data Structures and Alogrithms

Tue, 29 Nov 2016 00:16:23 +0400

Important Caveat Emptor!

Data Structures and Alogrithms

Here is the truth about data structures and algorithms: a data structure is a bet about which questions you’ll ask most, and an algorithm is how you answer them. Pick the wrong bet and even brilliant code crawls.

What it is, in plain words

This page sits beside Algorithms as a more structure-focused map. The classic cast:

Structure	Good at	Weak at
Array / slice	Index access $O(1)$	Insert at front
Dynamic array (Vec, list)	Amortised append	Mid inserts
Linked list	Splice if you have a pointer	Random access
Stack / queue	LIFO / FIFO patterns	Search
Hash table	Lookup/insert average $O(1)$	Range queries, ordering
BST / tree map	Ordered ops $O(\log n)$	Cache locality vs hash
Heap	Min/max repeatedly	Arbitrary delete
Graph (adj list)	Sparse relations	Dense matrix ops
Trie	Prefix search	Memory use

Why it matters

Databases, compilers, games, routers and ML frameworks are data-structure museums. When a feature is slow, the fix is often “wrong structure” rather than “buy a bigger CPU”. See also big-O notes on the Algorithms page.

Computer Science and Theory

Tue, 29 Nov 2016 00:14:23 +0400

Important Caveat Emptor!

Computer Science and Theory

Here is the truth about computer science: it is not the study of computers any more than astronomy is the study of telescopes. It is the study of processes – what can be computed, how fast, how reliably, and how we express those processes so others (and machines) can carry them out.

What it is, in plain words

Computer science (CS) is the body of ideas that underpins programming, systems and computation itself. A useful map of the territory: