Author: Herb Kunze
Publisher: CRC Press
ISBN: 1000907872
Category : Technology & Engineering
Languages : en
Pages : 530
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
Engineering Mathematics and Artificial Intelligence
Author: Herb Kunze
Publisher: CRC Press
ISBN: 1000907872
Category : Technology & Engineering
Languages : en
Pages : 530
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
Publisher: CRC Press
ISBN: 1000907872
Category : Technology & Engineering
Languages : en
Pages : 530
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
Artificial Intelligence and Applied Mathematics in Engineering Problems
Author: D. Jude Hemanth
Publisher: Springer Nature
ISBN: 3030361780
Category : Technology & Engineering
Languages : en
Pages : 1105
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.
Publisher: Springer Nature
ISBN: 3030361780
Category : Technology & Engineering
Languages : en
Pages : 1105
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.
Mathematics for Machine Learning
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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.
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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.
Data-Driven Science and Engineering
Author: Steven L. Brunton
Publisher: Cambridge University Press
ISBN: 1009098489
Category : Computers
Languages : en
Pages : 615
Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Publisher: Cambridge University Press
ISBN: 1009098489
Category : Computers
Languages : en
Pages : 615
Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Deep Learning for Coders with fastai and PyTorch
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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
Advanced Engineering Mathematics with Mathematica and MATLAB
Author: Reza Malek-Madani
Publisher: Addison Wesley
ISBN:
Category : Computers
Languages : en
Pages : 612
Book Description
See previous listing for contents.
Publisher: Addison Wesley
ISBN:
Category : Computers
Languages : en
Pages : 612
Book Description
See previous listing for contents.
Emerging Artificial Intelligence Applications in Computer Engineering
Author: Ilias G. Maglogiannis
Publisher: IOS Press
ISBN: 1586037803
Category : Computers
Languages : en
Pages : 420
Book Description
Provides insights on how computer engineers can implement artificial intelligence (AI) in real world applications. This book presents practical applications of AI.
Publisher: IOS Press
ISBN: 1586037803
Category : Computers
Languages : en
Pages : 420
Book Description
Provides insights on how computer engineers can implement artificial intelligence (AI) in real world applications. This book presents practical applications of AI.
Applications of Artificial Intelligence in Engineering
Author: Xiao-Zhi Gao
Publisher: Springer Nature
ISBN: 9813346043
Category : Technology & Engineering
Languages : en
Pages : 922
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.
Publisher: Springer Nature
ISBN: 9813346043
Category : Technology & Engineering
Languages : en
Pages : 922
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.
Machine Learning for Financial Engineering
Author: György Ottucsák
Publisher: World Scientific
ISBN: 1848168136
Category : Business & Economics
Languages : en
Pages : 261
Book Description
Preface v 1 On the History of the Growth-Optimal Portfolio M.M. Christensen 1 2 Empirical Log-Optimal Portfolio Selections: A Survey L. Györfi Gy. Ottucsáak A. Urbán 81 3 Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Györfi H. Walk 119 4 Growth-Optimal Portfoho Selection with Short Selling and Leverage M. Horváth A. Urbán 153 5 Nonparametric Sequential Prediction of Stationary Time Series L. Györfi Gy. Ottucsák 179 6 Empirical Pricing American Put Options L. Györfi A. Telcs 227 Index 249.
Publisher: World Scientific
ISBN: 1848168136
Category : Business & Economics
Languages : en
Pages : 261
Book Description
Preface v 1 On the History of the Growth-Optimal Portfolio M.M. Christensen 1 2 Empirical Log-Optimal Portfolio Selections: A Survey L. Györfi Gy. Ottucsáak A. Urbán 81 3 Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Györfi H. Walk 119 4 Growth-Optimal Portfoho Selection with Short Selling and Leverage M. Horváth A. Urbán 153 5 Nonparametric Sequential Prediction of Stationary Time Series L. Györfi Gy. Ottucsák 179 6 Empirical Pricing American Put Options L. Györfi A. Telcs 227 Index 249.
Visual Group Theory
Author: Nathan Carter
Publisher: American Mathematical Soc.
ISBN: 1470464330
Category : Education
Languages : en
Pages : 295
Book Description
Recipient of the Mathematical Association of America's Beckenbach Book Prize in 2012! Group theory is the branch of mathematics that studies symmetry, found in crystals, art, architecture, music and many other contexts, but its beauty is lost on students when it is taught in a technical style that is difficult to understand. Visual Group Theory assumes only a high school mathematics background and covers a typical undergraduate course in group theory from a thoroughly visual perspective. The more than 300 illustrations in Visual Group Theory bring groups, subgroups, homomorphisms, products, and quotients into clear view. Every topic and theorem is accompanied with a visual demonstration of its meaning and import, from the basics of groups and subgroups through advanced structural concepts such as semidirect products and Sylow theory.
Publisher: American Mathematical Soc.
ISBN: 1470464330
Category : Education
Languages : en
Pages : 295
Book Description
Recipient of the Mathematical Association of America's Beckenbach Book Prize in 2012! Group theory is the branch of mathematics that studies symmetry, found in crystals, art, architecture, music and many other contexts, but its beauty is lost on students when it is taught in a technical style that is difficult to understand. Visual Group Theory assumes only a high school mathematics background and covers a typical undergraduate course in group theory from a thoroughly visual perspective. The more than 300 illustrations in Visual Group Theory bring groups, subgroups, homomorphisms, products, and quotients into clear view. Every topic and theorem is accompanied with a visual demonstration of its meaning and import, from the basics of groups and subgroups through advanced structural concepts such as semidirect products and Sylow theory.