Neural Networks and Deep Learning : A Textbook /
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concep...
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| Format: | text |
| Language: | eng |
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Cham, Switzerland : Springer,
2018
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| _version_ | 1826471378316951552 |
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| author | Aggarwal, Charu C., author 275428 |
| author_facet | Aggarwal, Charu C., author 275428 |
| author_sort | Aggarwal, Charu C., author 275428 |
| collection | OCEAN |
| description | This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. |
| first_indexed | 2024-03-05T17:02:29Z |
| format | text |
| id | KOHA-OAI-TEST:599185 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-03-05T17:02:29Z |
| publishDate | 2018 |
| publisher | Cham, Switzerland : Springer, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:5991852022-08-09T07:10:48ZNeural Networks and Deep Learning : A Textbook / Aggarwal, Charu C., author 275428 textCham, Switzerland : Springer,2018©2018engThis book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered.Includes bibliographical references and index.An introduction to neural networks -- Machine learning with shallow neural networks -- Training deep neural networks -- Teaching deep learners to generalize -- Radial basis function networks -- Restricted Boltzmann machines -- Recurrent neural networks -- Convolution neural networks -- Deep reinforcement learning -- Advanced topics in deep learningThis book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered.PSZJBNeural networks (Computer science)Machine learningURN:ISBN:9783319944623 |
| spellingShingle | Neural networks (Computer science) Machine learning Aggarwal, Charu C., author 275428 Neural Networks and Deep Learning : A Textbook / |
| title | Neural Networks and Deep Learning : A Textbook / |
| title_full | Neural Networks and Deep Learning : A Textbook / |
| title_fullStr | Neural Networks and Deep Learning : A Textbook / |
| title_full_unstemmed | Neural Networks and Deep Learning : A Textbook / |
| title_short | Neural Networks and Deep Learning : A Textbook / |
| title_sort | neural networks and deep learning a textbook |
| topic | Neural networks (Computer science) Machine learning |
| work_keys_str_mv | AT aggarwalcharucauthor275428 neuralnetworksanddeeplearningatextbook |