國立虎尾科技大學 |

Neural networks and deep learning = a textbook /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Neural networks and deep learning/ by Charu C. Aggarwal.
其他題名:	a textbook /
作者:	Aggarwal, Charu C.
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	xxiii, 497 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Neural networks (Computer science) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-94463-0
ISBN:	9783319944630

Neural networks and deep learning = a textbook /
Aggarwal, Charu C.

Neural networks and deep learninga textbook /[electronic resource] :by Charu C. Aggarwal. - Cham :Springer International Publishing :2018. - xxiii, 497 p. :ill. (some col.), digital ;24 cm.

1 An Introduction to Neural Networks -- 2 Machine Learning with Shallow Neural Networks -- 3 Training Deep Neural Networks -- 4 Teaching Deep Learners to Generalize -- 5 Radical Basis Function Networks -- 6 Restricted Boltzmann Machines -- 7 Recurrent Neural Networks -- 8 Convolutional Neural Networks -- 9 Deep Reinforcement Learning -- 10 Advanced Topics in Deep Learning.

This book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

ISBN: 9783319944630

Standard No.: 10.1007/978-3-319-94463-0doiSubjects--Topical Terms:

528588
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

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520 $a This book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
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