Bộ sách luyện thi ETS 2023 LC&RC
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Neural Networks and Deep Learning: A Textbook, 2nd Edition (Sách keo gáy, bìa mềm)
Categories:Computers - Artificial Intelligence (AI)
Year:2023
Edition:2nd ed. 2023
Language:english
Pages:541
This textbook covers both classical and modern models in deep
learning and includes examples and exercises throughout the chapters.
Deep learning methods for various data domains, such as text, images,
and graphs are presented in detail. The chapters of this book span
three categories:
The basics of neural networks: The backpropagation algorithm is discussed in Chapter 2.
Many
traditional machine learning models can be understood as special cases
of neural networks. Chapter 3 explores the connections 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.
Fundamentals of neural networks:
A detailed discussion of training and regularization is provided in
Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF)
networks and restricted Boltzmann machines.
Advanced topics in neural networks:
Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional
neural networks, and graph neural networks. Several advanced topics like
deep reinforcement learning, attention mechanisms, transformer
networks, Kohonen self-organizing maps, and generative adversarial
networks are introduced in Chapters 11 and 12.
The
textbook is written for graduate students and upper under graduate
level students. Researchers and practitioners working within this
related field will want to purchase this as well.
Where
possible, an application-centric view is highlighted in order to
provide an understanding of the practical uses of each class of
techniques.
The second edition is substantially
reorganized and expanded with separate chapters on backpropagation and
graph neural networks. Many chapters have been significantly revised
over the first edition.
Greater focus is placed on modern
deep learning ideas such as attention mechanisms, transformers, and
pre-trained language models.
