Inbunden, Engelska, 2020
1 095 kr
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Ethem Alpaydin – författare
Visar alla böcker från författaren Ethem Alpaydin. Handla med fri frakt och snabb leverans.
10 produkter
10 produkter
- Inbunden, Engelska, 2025
1 095 kr
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- E-bokEngelska, 2020
1 103 kr
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A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks.
The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.
- E-bokEngelska, 2021
235 kr
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- Häftad, Engelska, 2021
190 kr
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A concise primer on machine learning—computer programs that learn from data and the basis of applications like voice recognition and driverless cars.No in-depth knowledge of math or programming required!Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don’t yet use every day, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of “the new AI.” This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias.Alpaydin explains that as Big Data has grown, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. He covers:• The evolution of machine learning• Important learning algorithms and example applications• Using machine learning algorithms for pattern recognition• Artificial neural networks inspired by the human brain• Algorithms that learn associations between instances• Reinforcement learning• Transparency, explainability, and fairness in machine learning• The ethical and legal implicates of data-based decision makingA comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programming—making it accessible for everyday readers and easily adoptable for classroom syllabi. - Häftad, Tyska, 2022
796 kr
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- E-bokPDF, Tyska, 2022
919 kr
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Maschinelles Lernen ist die künstliche Generierung von Wissen aus Erfahrung. Dieses Buch diskutiert Methoden aus den Bereichen Statistik, Mustererkennung und kombiniert die unterschiedlichen Ansätze, um effiziente Lösungen zu finden. Diese Auflage bietet ein neues Kapitel über Deep Learning und erweitert die Inhalte über mehrlagige Perzeptrone und bestärkendes Lernen. Eine neue Sektion über erzeugende gegnerische Netzwerke ist ebenfalls dabei.
- E-bokTyska, 2022
949 kr
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Maschinelles Lernen ist die künstliche Generierung von Wissen aus Erfahrung. Dieses Buch diskutiert Methoden aus den Bereichen Statistik, Mustererkennung und kombiniert die unterschiedlichen Ansätze, um effiziente Lösungen zu finden. Diese Auflage bietet ein neues Kapitel über Deep Learning und erweitert die Inhalte über mehrlagige Perzeptrone und bestärkendes Lernen. Eine neue Sektion über erzeugende gegnerische Netzwerke ist ebenfalls dabei.
648 kr
Skickas inom 10-15 vardagar
This book constitutes the refereed proceedings of the joint International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003, held in Istanbul, Turkey, in June 2003.The 138 revised full papers were carefully reviewed and selected from 346 submissions. The papers are organized in topical sections on learning algorithms, support vector machine and kernel methods, statistical data analysis, pattern recognition, vision, speech recognition, robotics and control, signal processing, time-series prediction, intelligent systems, neural network hardware, cognitive science, computational neuroscience, context aware systems, complex-valued neural networks, emotion recognition, and applications in bioinformatics.- E-bokPDF, Engelska, 2003
732 kr
Läs direkt efter köp
This book constitutes the refereed proceedings of the joint International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003, held in Istanbul, Turkey, in June 2003.
The 138 revised full papers were carefully reviewed and selected from 346 submissions. The papers are organized in topical sections on learning algorithms, support vector machine and kernel methods, statistical data analysis, pattern recognition, vision, speech recognition, robotics and control, signal processing, time-series prediction, intelligent systems, neural network hardware, cognitive science, computational neuroscience, context aware systems, complex-valued neural networks, emotion recognition, and applications in bioinformatics.
