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Neural Networks and Deep Learning

Name: Neural Networks and Deep Learning
Brand: Springer, Berlin
SKU: 9783031296413
Price: 28230 HUF
Availability: InStock

A Textbook

Szerző: Charu C. Aggarwal

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer, Berlin

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

28 230 Ft

This textbook covers both classical and modern models in deep learning and includes examples and exe...

Információk a könyvről

Szerző

Charu C. Aggarwal

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2023

oldal

556

EAN

9783031296413

Enbook ID

43050075

Kiadó

Springer, Berlin

Súly

1214

Méretek

178 x 254

Teljes leírás

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.

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Neural Networks and Deep Learning

A Textbook

Információk a könyvről

Teljes leírás

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