| 书目名称 | Neural Networks and Deep Learning |
| 副标题 | A Textbook |
| 编辑 | Charu C. Aggarwal |
| 视频video | |
| 概述 | This book covers the theory and algorithms of deep learning and it provides detailed discussions of the relationships of neural networks with traditional machine learning algorithms..The mathematical |
| 图书封面 |  |
| 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. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:..The basics of neural networks: . Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditio |
| 出版日期 | Textbook 20181st edition |
| 关键词 | Deep Learning; Machine Learning; Radial Basis Function Networks; Restricted Boltzmann Machines; Recurren |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-94463-0 |
| isbn_softcover | 978-3-030-06856-1 |
| isbn_ebook | 978-3-319-94463-0 |
| copyright | Springer International Publishing AG, part of Springer Nature 2018 |
发表于 2025-3-21 17:53:57
