Neural networks and deep training
Please sign in so that we can notify you about a reply
The book discusses both classic and modern models of deep learning. The chapters of the book can be divided into three groups.
- the basics of neural networks. The essence of many traditional models of machine learning can be understood by considering them as special cases of neural networks. In the first two chapters, the main emphasis is on an understanding of the relationship of traditional machine learning and neural networks. It will be shown that the method of reference vectors, linear and logistics regression, singular decomposition, factorization of matrices and recommendation systems are just such particular cases. Along with them, such relatively new methods of designing features as Word2VEC are also considered.
- fundamental concepts of neural networks. Chapters 3 and 4 are devoted to a detailed discussion of the processes of training and regulator of neural networks. Chapters 5 and 6 discusses the networks of radial-basic functions (RBF) and limited Boltzmann machines.
- additional issues of neural networks. Chapters 7 and 8 discusses recurrent and adverse neural networks. Chapters 9 and 10 are devoted to more complex topics, such as deep training with reinforcements, turing neural machines, self-organizing cards of Kohonen and generative and consistent networks.
The book is intended for senior students, researchers and practitioners. Where it is possible, the author pays special attention to applied aspects of using each class of methods
- the basics of neural networks. The essence of many traditional models of machine learning can be understood by considering them as special cases of neural networks. In the first two chapters, the main emphasis is on an understanding of the relationship of traditional machine learning and neural networks. It will be shown that the method of reference vectors, linear and logistics regression, singular decomposition, factorization of matrices and recommendation systems are just such particular cases. Along with them, such relatively new methods of designing features as Word2VEC are also considered.
- fundamental concepts of neural networks. Chapters 3 and 4 are devoted to a detailed discussion of the processes of training and regulator of neural networks. Chapters 5 and 6 discusses the networks of radial-basic functions (RBF) and limited Boltzmann machines.
- additional issues of neural networks. Chapters 7 and 8 discusses recurrent and adverse neural networks. Chapters 9 and 10 are devoted to more complex topics, such as deep training with reinforcements, turing neural machines, self-organizing cards of Kohonen and generative and consistent networks.
The book is intended for senior students, researchers and practitioners. Where it is possible, the author pays special attention to applied aspects of using each class of methods
Author:
Author:Chara Agggarval
Cover:
Cover:Hard
Category:
- Category:Computer & Technology
Publication language:
Publication Language:Russian
ISBN:
ISBN:978-5-907203-01-3
No reviews found