000			02155nam\a2200301\a\4500
001			57642
005			20251211152859.0
008			251211t20212022caua b 001 0 eng d
010			_a 2021939724
020			_a9781718501904 _q(pbk.)
020			_a1718501900 _q(pbk.)
040			_aUKMGB _beng _cUKMGB _erda _dOCLCF _dJRZ _dBDX _dIMD _dOCLCO _dBD-DhIUB
082	0	4	_a006.310151 _223 _bK689m
100	1		_aKneusel, Ronald T., _eauthor.
245	1	0	_aMath for deep learning : _bwhat you need to know to understand neural networks / _cby Ronald T. Kneusel.
260			_aSan Francisco: _bNo starch press, _c2022
300			_axxv, 316 pages : _billustrations ; _c24 cm
504			_aIncludes bibliographical references and index.
505	0		_aSetting the stage -- Probability -- More probability -- Statistics -- Linear algebra -- More linear algebra -- Differential calculus -- Matrix calculus -- Data flow in neural networks -- Backpropagation -- Gradient descent -- Going further.
520			_aTo truly understand the power of deel learning, you need to grasp the mathematical concepts that make it tick. "Math for deep learning" will give you a working knowledge of probability, statistics, linear algebra, and differential calculus-- the essential math subfields required to practice deep learning successfully. Each subfield is explained with Python code and hands-on, real-world examples that bridge the gap between pure mathematics and its applications in deep learning. The book begins with fundamentals such as Bayes' theorem before progressing to more advanced concepts like training neural networks using vectors, matrices, and derivatives of functions. You'll then put all this math to use as you explore and implement backpropagation and gradient descent-- the foundational algorithms that have enabled the AI revolution.
526			_aCSE _bps _lREF
541			_aRisaam
650		0	_aMachine learning _xMathematics.
650		0	_aNeural networks (Computer science) _xMathematics.
650		7	_aNeural networks (Computer science) _xMathematics. _2fast
942			_2ddc _cBK
999			_c57642 _d57816