Vijay Anant vijayanant

Haskell Resources

Git Branching and Realeasing

AIM

To make below listed activites simple (from SCM point of view)

Adding new features
Fixing bugs
Preparing for release
Deploying to production
Applying hotfixes
Versioning

Curriculum: Mathematical Foundations of AI

This is a personalized, self-paced roadmap for mastering the mathematical foundations of Machine Learning (ML) and Artificial Intelligence (AI). It is designed to build a strong "intuition-first" theoretical base, immediately backed by practical Python/NumPy implementations and empirical experiments.

Primary Theoretical Sources & Video Series

[MFML] Mathematical Foundations of Machine Learning by Prof. Prathosh AP (IISc)
[PF] Probability Foundations by Prof. Krishna Jagannathan (IIT Madras)
[Prathosh-Prob] Probability Theory by Prof. Prathosh (IISc)
[3B1B-LA] 3Blue1Brown: Essence of Linear Algebra

	{-# LANGUAGE TypeFamilies
	, DataKinds
	, PolyKinds
	, TypeInType
	, TypeOperators
	, UndecidableInstances
	, RankNTypes
	#-}

	# Problem Statement
	# cons(a, b) constructs a pair, and car(pair) and cdr(pair) returns the first and last element of that pair.
	# For example, car(cons(3, 4)) returns 3, and cdr(cons(3, 4)) returns 4.
	# Given the below implementation for cons( ), please implement car & cdr

	def cons(a, b):
	def pair(f):
	return f(a, b)
	return pair

	trait Summable {
	fn zero() -> Self;
	fn add(&self, other: &Self) -> Self;
	}

	impl Summable for i32 {
	fn zero() -> Self {
	0
	}
	fn add(&self, other: &Self) -> Self {

	def can_user_act_on_document(user, document, action):
	if user.is_admin:
	return True
	if action == "view":
	return document.owner_id == user.id
	if action == "edit":
	return document.owner_id == user.id
	return False # unknown action

	# growing conditionals and nested logic

	data Point = Pt Int Int
	data Expr a = Number Integer \| Boolean Bool