Before Machine Learning Volume 2 - Calculus for A.I. (Record no. 16054)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250710182907.0 |
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| 024 80 - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 9781836200680 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Jorge Brasil |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Before Machine Learning Volume 2 - Calculus for A.I. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2024-11-22. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2024-11-22 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 314. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <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 ## - SYSTEM DETAILS NOTE | |
| System details note | Data in extended ASCII character set. |
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| System details note | Mode of access: Internet. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Before Machine Learning Volume 2 - Calculus for A.I |
| Place, publisher, and date of publication | GB,Packt,2024-11-22 |
| Physical description | 314 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/482402">https://learning.packt.com/product/482402</a> |
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