Transfer learning:
Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such syst...
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| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2020
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| Links: | https://doi.org/10.1017/9781139061773 |
| Summary: | Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers. |
| Physical Description: | 1 Online-Ressource (xi, 379 Seiten) |
| ISBN: | 9781139061773 |
Staff View
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| 520 | |a Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers. | ||
| 700 | 1 | |a Dai, Wenyuan |d 1983- | |
| 700 | 1 | |a Pan, Sinno Jialin |d 1980- | |
| 700 | 1 | |a Zhang, Yu |d 1982- | |
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| spelling | Yang, Qiang 1961- Transfer learning Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan Cambridge Cambridge University Press 2020 1 Online-Ressource (xi, 379 Seiten) txt c cr Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers. Dai, Wenyuan 1983- Pan, Sinno Jialin 1980- Zhang, Yu 1982- Erscheint auch als Druck-Ausgabe 9781107016903 |
| spellingShingle | Yang, Qiang 1961- Transfer learning |
| title | Transfer learning |
| title_auth | Transfer learning |
| title_exact_search | Transfer learning |
| title_full | Transfer learning Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan |
| title_fullStr | Transfer learning Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan |
| title_full_unstemmed | Transfer learning Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan |
| title_short | Transfer learning |
| title_sort | transfer learning |
| work_keys_str_mv | AT yangqiang transferlearning AT daiwenyuan transferlearning AT pansinnojialin transferlearning AT zhangyu transferlearning |