000			03045nam a2200265uu 4500
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024	8	0	_a9781836200680
040			_aPACKT _cPACKT
041			_aen
044			_aGB
100	0		_aJorge Brasil _eauthor.
710	2		_aPACKT
773	0		_tBefore Machine Learning Volume 2 - Calculus for A.I _dGB,Packt,2024-11-22 _h314
245	0	0	_aBefore Machine Learning Volume 2 - Calculus for A.I.
300			_a314.
377			_aen
260			_aGB: _bPackt, _c2024-11-22.
263			_a2024-11-22
264		1	_aGB: _bPackt,
520			_a<p><b>Deepen your calculus foundation for AI and machine learning with essential concepts like derivatives, integrals, and multivariable calculus, all applied directly to neural networks and optimization.</b></p><h4>Key Features</h4><ul><li>A step-by-step guide to calculus concepts tailored for AI and machine learning applications</li><li>Clear explanations of advanced topics like Taylor Series, gradient descent, and backpropagation</li><li>Practical insights connecting calculus principles directly to neural networks and data science</li></ul><h4>Book Description</h4>This book takes readers on a structured journey through calculus fundamentals essential for AI. Starting with "Why Calculus?" it introduces key concepts like functions, limits, and derivatives, providing a solid foundation for understanding machine learning. As readers progress, they will encounter practical applications such as Taylor Series for curve fitting, gradient descent for optimization, and L'Hôpital's Rule for managing undefined expressions. Each chapter builds up from core calculus to multidimensional topics, making complex ideas accessible and applicable to AI. The final chapters guide readers through multivariable calculus, including advanced concepts like the gradient, Hessian, and backpropagation, crucial for neural networks. From optimizing models to understanding cost functions, this book equips readers with the calculus skills needed to confidently tackle AI challenges, offering insights that make complex calculus both manageable and deeply relevant to machine learning.<h4>What you will learn</h4><ul><li>Explore the essentials of calculus for machine learning</li><li>Calculate derivatives and apply them in optimization tasks</li><li>Analyze functions, limits, and continuity in data science</li><li>Apply Taylor Series for predictive curve modeling</li><li>Use gradient descent for effective cost-minimization</li><li>Implement multivariable calculus in neural networks</li></ul><h4>Who this book is for</h4> Aspiring AI engineers, machine learning students, and data scientists will find this book valuable for building a strong calculus foundation. A basic understanding of calculus is beneficial, but the book introduces essential concepts gradually for all levels.
538			_aData in extended ASCII character set.
538			_aMode of access: Internet.
856	4	0	_uhttps://learning.packt.com/product/482402
999			_c16054 _d16054