| 書(shū)目名稱 | Neural Networks and Deep Learning |
| 副標(biāo)題 | A Textbook |
| 編輯 | Charu C. Aggarwal |
| 視頻video | http://file.papertrans.cn/664/663703/663703.mp4 |
| 概述 | Simple and intuitive discussions of neural networks and deep learning.Provides mathematical details without losing the reader in complexity.Includes exercises and examples.Discusses both traditional n |
| 圖書(shū)封面 |  |
| 描述 | .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.?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 regressio |
| 出版日期 | Textbook 2023Latest edition |
| 關(guān)鍵詞 | Neural networks; Deep Learning; Machine Learning; Artificial Intelligence; Transformers; Pre-Trained Lang |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-3-031-29642-0 |
| isbn_softcover | 978-3-031-29644-4 |
| isbn_ebook | 978-3-031-29642-0 |
| copyright | Springer Nature Switzerland AG 2023 |
|Archiver|手機(jī)版|小黑屋|
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