Engineering Mathematics and Artificial Intelligence PDF Download
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Author: Herb Kunze
Publisher: CRC Press
ISBN: 1000907872
Category : Technology & Engineering
Languages : en
Pages : 530
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Book Description
Explains the theory behind Machine Learning and highlights how Mathematics can be used in Artificial Intelligence Illustrates how to improve existing algorithms by using advanced mathematics and discusses how Machine Learning can support mathematical modeling Captures how to simulate data by means of artificial neural networks and offers cutting-edge Artificial Intelligence technologies Emphasizes the classification of algorithms, optimization methods, and statistical techniques Explores future integration between Machine Learning and complex mathematical techniques
Author: Herb Kunze
Publisher: CRC Press
ISBN: 1000907872
Category : Technology & Engineering
Languages : en
Pages : 530
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Book Description
Explains the theory behind Machine Learning and highlights how Mathematics can be used in Artificial Intelligence Illustrates how to improve existing algorithms by using advanced mathematics and discusses how Machine Learning can support mathematical modeling Captures how to simulate data by means of artificial neural networks and offers cutting-edge Artificial Intelligence technologies Emphasizes the classification of algorithms, optimization methods, and statistical techniques Explores future integration between Machine Learning and complex mathematical techniques
Author: D. Jude Hemanth
Publisher: Springer Nature
ISBN: 3030361780
Category : Technology & Engineering
Languages : en
Pages : 1105
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Book Description
This book features research presented at the 1st International Conference on Artificial Intelligence and Applied Mathematics in Engineering, held on 20–22 April 2019 at Antalya, Manavgat (Turkey). In today’s world, various engineering areas are essential components of technological innovations and effective real-world solutions for a better future. In this context, the book focuses on problems in engineering and discusses research using artificial intelligence and applied mathematics. Intended for scientists, experts, M.Sc. and Ph.D. students, postdocs and anyone interested in the subjects covered, the book can also be used as a reference resource for courses related to artificial intelligence and applied mathematics.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author: Steven L. Brunton
Publisher: Cambridge University Press
ISBN: 1009098489
Category : Computers
Languages : en
Pages : 615
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Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Edward A. Bender
Publisher: Wiley-IEEE Computer Society Press
ISBN: 9780818672002
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
Mathematical Methods in Artificial Intelligence introduces the student to the important mathematical foundations and tools in AI and describes their applications to the design of AI algorithms. This useful text presents an introductory AI course based on the most important mathematics and its applications. It focuses on important topics that are proven useful in AI and involve the most broadly applicable mathematics. The book explores AI from three different viewpoints: goals, methods or tools, and achievements and failures. Its goals of reasoning, planning, learning, or language understanding and use are centered around the expert system idea. The tools of AI are presented in terms of what can be incorporated in the data structures. The book looks into the concepts and tools of limited structure, mathematical logic, logic-like representation, numerical information, and nonsymbolic structures. The text emphasizes the main mathematical tools for representing and manipulating knowledge symbolically. These are various forms of logic for qualitative knowledge, and probability and related concepts for quantitative knowledge. The main tools for manipulating knowledge nonsymbolically, as neural nets, are optimization methods and statistics. This material is covered in the text by topics such as trees and search, classical mathematical logic, and uncertainty and reasoning. A solutions diskette is available, please call for more information.
Author: Xiao-Zhi Gao
Publisher: Springer Nature
ISBN: 9813346043
Category : Technology & Engineering
Languages : en
Pages : 922
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Book Description
This book presents best selected papers presented at the First Global Conference on Artificial Intelligence and Applications (GCAIA 2020), organized by the University of Engineering & Management, Jaipur, India, during 8–10 September 2020. The proceeding will be targeting the current research works in the domain of intelligent systems and artificial intelligence.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309046483
Category : Technology & Engineering
Languages : en
Pages : 145
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Book Description
Computational mechanics is a scientific discipline that marries physics, computers, and mathematics to emulate natural physical phenomena. It is a technology that allows scientists to study and predict the performance of various productsâ€"important for research and development in the industrialized world. This book describes current trends and future research directions in computational mechanics in areas where gaps exist in current knowledge and where major advances are crucial to continued technological developments in the United States.
Author: Roberto Battiti
Publisher: Createspace Independent Publishing Platform
ISBN: 9781496034021
Category : Artificial intelligence
Languages : en
Pages : 0
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Book Description
Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex and dynamic problems. The LION way is about increasing the automation level and connecting data directly to decisions and actions. More power is directly in the hands of decision makers in a self-service manner, without resorting to intermediate layers of data scientists. LION is a complex array of mechanisms, like the engine in an automobile, but the user (driver) does not need to know the inner workings of the engine in order to realize its tremendous benefits. LION's adoption will create a prairie fire of innovation which will reach most businesses in the next decades. Businesses, like plants in wildfire-prone ecosystems, will survive and prosper by adapting and embracing LION techniques, or they risk being transformed from giant trees to ashes by the spreading competition.
Author: Ilias G. Maglogiannis
Publisher: IOS Press
ISBN: 1586037803
Category : Computers
Languages : en
Pages : 420
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Book Description
Provides insights on how computer engineers can implement artificial intelligence (AI) in real world applications. This book presents practical applications of AI.
