000			02070nam a2200301 i 4500
005			20240603141703.0
008			220118s2020 nyua b 001 0 eng d
020			_a9781984637215
040			_cCSPC
050	0	4	_aQA162
082	0	4	_a512.02 _bAb892
245	0	0	_aAbstract algebra.
264		1	_aNew York : _b3G E-Learning, _c2020.
300			_axiv, 314 pages : _billustrations (some color) ; _c27 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
504			_aIncludes bibliographical references and index.
505	0		_aPreliminaries -- Groups -- Rings -- Fields -- Some important theorems -- The fundamental theorem of Galois theory.
520			_aThe concepts of Linear Algebra are crucial for understanding the theory behind Machine Learning, especially for Deep Learning. They give you better intuition for how algorithms really work under the hood, which enables you to make better decisions. Abstract algebra is a broad field of mathematics, concerned with algebraic structures such as groups, rings, vector spaces, and algebras. In a rough way, abstract algebra is the study of what happens when certain properties of number systems are abstracted out; for instance, altering the definitions od the basic arithmetic operations result in a structure known as a ring, so long as the operations are consistent. A certain amount of mathematical maturity is necessary to find and study applications of abstract algebra. A basic knowledge of set theory, mathematical induction, equivalence relations, and matrices is a must. Even more important is the ability to read and understant mathematical proofs. In this book, we will outline the background needed in abstract algebra.
650		4	_aAlgebra, Abstract.
710			_a3G E-Learning
940			_aLenie
940			_amae
942			_cBK _2ddc _h512.02 Ab892 _kCIR _m2020 _n0
999			_c11689 _d11689