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  • Ký hiệu PL/XG: 006.32 A266
    Nhan đề: Neural networks and deep learning :
ISBN 9783319944623
DDC 006.32
Tác giả CN Aggarwal, Charu C.
Nhan đề Neural networks and deep learning : a textbook / Charu C Aggarwal
Thông tin xuất bản Cham, Switzerland : Springer, 2018.
Mô tả vật lý 512 pages. : illustrations
Tóm tắt 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
Thuật ngữ chủ đề Computer science
Thuật ngữ chủ đề Neural networks (Computer science)
Thuật ngữ chủ đề Artificial intelligence
Thuật ngữ chủ đề Machine learning
Khoa Khoa Công nghệ Thông tin
Khoa Khoa Cơ khí - Điện - Điện tử - Ô tô
Địa chỉ Thư Viện Đại học Nguyễn Tất Thành
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041 |aeng
044 |asz
082 |a006.32|bA266|223
100 |aAggarwal, Charu C.
245 |aNeural networks and deep learning : |ba textbook / |cCharu C Aggarwal
260 |aCham, Switzerland : |bSpringer, |c2018.
300 |a512 pages. : |billustrations
504 |aIncludes bibliographical references (pages 474-507) and index.
520 |aThis 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
541 |aSpringer
650 |aComputer science
650 |aNeural networks (Computer science)
650 |aArtificial intelligence
650 |aMachine learning
690 |aKhoa Công nghệ Thông tin
690|aKhoa Cơ khí - Điện - Điện tử - Ô tô
691|aCơ điện tử
691|aKỹ thuật Điện - Điện tử
691|aMạng máy tính & truyền thông dữ liệu
852 |aThư Viện Đại học Nguyễn Tất Thành
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